Non-clinal sims analysis
KE Lotterhos
9/20/2021
Make sure to set the working directory in the above code chunk
Read in data
out.df <- read.table(file = "summary_20220428_20220726.txt", header=TRUE)
head(out.df)## seed n_samp_tot n_samp_per_pop sd_fitness_among_inds sd_fitness_among_pops
## 1 1231094 1000 10 0.05272602 0.01977273
## 2 1231095 1000 10 0.04519923 0.01541145
## 3 1231096 1000 10 0.05348903 0.02022721
## 4 1231097 1000 10 0.05446050 0.01731323
## 5 1231098 1000 10 0.05093029 0.01832588
## 6 1231099 1000 10 0.07552882 0.02341833
## final_LA K Bonf_alpha numCausalLowMAFsample all_corr_phen_temp
## 1 0.500854 2 1.918428e-06 39 0.9545790
## 2 0.505075 9 1.730523e-06 104 0.9545764
## 3 0.499265 9 1.508614e-06 22 0.9723022
## 4 0.499365 9 1.531065e-06 24 0.9720233
## 5 0.501054 6 6.395498e-06 12 0.9708565
## 6 0.478131 3 2.022981e-06 34 0.9247450
## subsamp_corr_phen_temp all_corr_phen_sal subsamp_corr_phen_sal
## 1 0.8893387 NA NA
## 2 0.9006961 NA NA
## 3 0.8873320 NA NA
## 4 0.8895298 NA NA
## 5 0.8921410 NA NA
## 6 0.8525464 NA NA
## num_causal_prefilter num_causal_postfilter num_non_causal num_neut_prefilter
## 1 2628 310 25753 26587
## 2 3144 382 28511 30284
## 3 3149 482 32661 33031
## 4 3131 470 32187 32548
## 5 1823 257 7561 7753
## 6 2948 364 24352 25004
## num_neut_postfilter num_neut_neutralgenome num_causal_temp num_causal_sal
## 1 26587 12867 310 0
## 2 30284 14060 382 0
## 3 33031 16379 480 0
## 4 32548 16243 469 0
## 5 7753 3749 257 0
## 6 25004 12197 364 0
## num_multiallelic meanFst va_temp_total va_sal_total Va_temp_sample
## 1 0 0.18039488 0.01185008 0 0.01356519
## 2 0 0.16985797 0.01343736 0 0.01276814
## 3 0 0.13396101 0.01405264 0 0.01370501
## 4 0 0.08714633 0.01337458 0 0.01305077
## 5 0 0.31813358 0.01605415 0 0.01622973
## 6 0 0.12616527 0.01930127 0 0.02072472
## Va_sal_sample nSNPs median_causal_temp_cor median_causal_sal_cor
## 1 0 26063 0.3580437 NA
## 2 0 28893 0.4138162 NA
## 3 0 33143 0.4157033 NA
## 4 0 32657 0.4112619 NA
## 5 0 7818 0.3925022 NA
## 6 0 24716 0.2974287 NA
## median_neut_temp_cor median_neut_sal_cor cor_VA_temp_prop cor_VA_sal_prop
## 1 0.3820531 0.04816897 0.8451605 0
## 2 0.4536498 0.04175877 0.7846421 0
## 3 0.4029319 0.03844065 0.7830061 0
## 4 0.3451512 0.04529330 0.7631297 0
## 5 0.4432208 0.05141380 0.8171677 0
## 6 0.2243095 0.19531471 0.6459740 0
## cor_TPR_temp cor_TPR_sal cor_FDR_allSNPs_temp cor_FDR_neutSNPs_temp
## 1 0.4612903 NA 0.9894449 0.9791241
## 2 0.5314136 NA 0.9886134 0.9769893
## 3 0.5416667 NA 0.9854586 0.9711015
## 4 0.5351812 NA 0.9833466 0.9669693
## 5 0.5408560 NA 0.9711199 0.9447316
## 6 0.3076923 NA 0.9813892 0.9575114
## cor_FDR_allSNPs_sal cor_FDR_neutSNPs_sal num_causal_sig_temp_corr
## 1 NA NA 143
## 2 NA NA 203
## 3 NA NA 260
## 4 1 NA 251
## 5 NA NA 139
## 6 1 1 112
## num_causal_sig_sal_corr num_notCausal_sig_temp_corr
## 1 0 13405
## 2 0 17625
## 3 0 17620
## 4 0 14821
## 5 0 4674
## 6 0 5906
## num_notCausal_sig_sal_corr num_neut_sig_temp_corr num_neut_sig_sal_corr
## 1 0 6707 0
## 2 0 8619 0
## 3 0 8737 0
## 4 1 7348 0
## 5 0 2376 0
## 6 4344 2524 2133
## cor_AUCPR_temp_allSNPs cor_AUCPR_temp_neutSNPs cor_AUCPR_sal_allSNPs
## 1 0.01064045 0.02148047 NA
## 2 0.01053868 0.02130552 NA
## 3 0.01286086 0.02546132 NA
## 4 0.01566591 0.03219810 NA
## 5 0.02696219 0.05312152 NA
## 6 0.02255358 0.06178401 NA
## cor_AUCPR_sal_neutSNPs cor_af_temp_noutliers cor_af_sal_noutliers
## 1 NA 13548 0
## 2 NA 17828 0
## 3 NA 17880 0
## 4 NA 15072 1
## 5 NA 4813 0
## 6 NA 6018 4344
## cor_FPR_temp_neutSNPs cor_FPR_sal_neutSNPs LEA3.2_lfmm2_Va_temp_prop
## 1 0.5212559 0.0000000 0.27826502
## 2 0.6130156 0.0000000 0.00000000
## 3 0.5334269 0.0000000 0.00000000
## 4 0.4523795 0.0000000 0.00000000
## 5 0.6337690 0.0000000 0.03610723
## 6 0.2069361 0.1748791 0.17163124
## LEA3.2_lfmm2_Va_sal_prop LEA3.2_lfmm2_TPR_temp LEA3.2_lfmm2_TPR_sal
## 1 NA 0.022580645 NA
## 2 NA 0.000000000 NA
## 3 NA 0.000000000 NA
## 4 NA 0.000000000 NA
## 5 NA 0.003891051 NA
## 6 NA 0.005494505 NA
## LEA3.2_lfmm2_FDR_allSNPs_temp LEA3.2_lfmm2_FDR_allSNPs_sal
## 1 0.9789790 NA
## 2 NA 1
## 3 NA 1
## 4 1.0000000 NA
## 5 0.9500000 NA
## 6 0.8666667 NA
## LEA3.2_lfmm2_FDR_neutSNPs_temp LEA3.2_lfmm2_FDR_neutSNPs_sal
## 1 0.9263158 NA
## 2 NA NA
## 3 NA NA
## 4 1.0000000 NA
## 5 0.8750000 NA
## 6 0.0000000 NA
## LEA3.2_lfmm2_AUCPR_temp_allSNPs LEA3.2_lfmm2_AUCPR_temp_neutSNPs
## 1 0.01339495 0.02655237
## 2 0.01367013 0.02786249
## 3 0.01426730 0.02851768
## 4 0.01310529 0.02513767
## 5 0.03463172 0.06672248
## 6 0.01437720 0.02928224
## LEA3.2_lfmm2_AUCPR_sal_allSNPs LEA3.2_lfmm2_AUCPR_sal_neutSNPs
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## LEA3.2_lfmm2_mlog10P_tempenv_noutliers LEA3.2_lfmm2_mlog10P_salenv_noutliers
## 1 333 0
## 2 0 1
## 3 0 1
## 4 6 0
## 5 20 0
## 6 15 0
## LEA3.2_lfmm2_num_causal_sig_temp LEA3.2_lfmm2_num_neut_sig_temp
## 1 7 88
## 2 0 0
## 3 0 0
## 4 0 2
## 5 1 7
## 6 2 0
## LEA3.2_lfmm2_num_causal_sig_sal LEA3.2_lfmm2_num_neut_sig_sal
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## LEA3.2_lfmm2_FPR_neutSNPs_temp LEA3.2_lfmm2_FPR_neutSNPs_sal RDA1_propvar
## 1 0.006839201 0 0.985
## 2 0.000000000 0 0.989
## 3 0.000000000 0 0.988
## 4 0.000123130 0 0.980
## 5 0.001867165 0 0.991
## 6 0.000000000 0 0.596
## RDA2_propvar RDA1_propvar_corr RDA2_propvar_corr RDA1_temp_cor RDA1_sal_cor
## 1 0.015 0.765 0.235 0.9999982 0.001912722
## 2 0.011 0.835 0.165 0.9999971 0.002425692
## 3 0.012 0.842 0.158 0.9999982 0.001888450
## 4 0.020 0.841 0.159 0.9999700 -0.007739672
## 5 0.009 0.816 0.184 0.9999640 0.008484251
## 6 0.404 0.597 0.403 -0.9360957 0.351745296
## RDA2_temp_cor RDA2_sal_cor RDA_Va_temp_prop RDA_Va_temp_prop_corr
## 1 -0.001912722 0.9999982 0.1248244 0.000000000
## 2 -0.002425692 0.9999971 0.3566111 0.017427370
## 3 -0.001888450 0.9999982 0.1750594 0.000000000
## 4 0.007739672 0.9999700 0.1339135 0.000000000
## 5 -0.008484251 0.9999640 0.3123098 0.036329553
## 6 -0.351745296 -0.9360957 0.3414487 0.004385126
## RDA_Va_sal_prop RDA_Va_sal_prop_corr RDA_TPR RDA_TPR_corr RDA_FDR_allSNPs
## 1 0 0 0.006451613 0.000000000 0.9918699
## 2 0 0 0.041884817 0.015706806 0.9953502
## 3 0 0 0.010373444 0.000000000 0.9957519
## 4 0 0 0.014893617 0.000000000 0.9941812
## 5 0 0 0.073929961 0.015564202 0.9850039
## 6 0 0 0.027472527 0.002747253 0.9889258
## RDA_FDR_allSNPs_corr num_RDA_sig_causal num_RDA_sig_neutral
## 1 1.0000000 2 118
## 2 0.9966979 16 1545
## 3 1.0000000 5 526
## 4 1.0000000 7 557
## 5 0.9833333 19 583
## 6 0.9979550 10 271
## num_RDA_sig_causal_corr num_RDA_sig_neutral_corr RDA_FDR_neutSNPs
## 1 0 200 0.9833333
## 2 6 867 0.9897502
## 3 0 458 0.9905838
## 4 0 577 0.9875887
## 5 4 122 0.9684385
## 6 1 213 0.9644128
## RDA_FDR_neutSNPs_corr RDA_AUCPR_allSNPs RDA_AUCPR_neutSNPs
## 1 1.0000000 0.008249728 0.01647883
## 2 0.9931271 0.008916531 0.01857113
## 3 1.0000000 0.009025124 0.01784801
## 4 1.0000000 0.009215389 0.01812750
## 5 0.9682540 0.025358473 0.05094037
## 6 0.9953271 0.014302424 0.02930863
## RDA_AUCPR_neutSNPs_corr RDA_FPR_neutSNPs RDA_FPR_neutSNPs_corr
## 1 0.01597697 0.009170747 0.01554364
## 2 0.01824752 0.109886202 0.06166430
## 3 0.01820267 0.032114293 0.02796264
## 4 0.01825734 0.034291695 0.03552299
## 5 0.05046366 0.155508136 0.03254201
## 6 0.01982718 0.022218578 0.01746331
## RDA_RDAmutpred_cor_tempEffect RDA_RDAmutpred_cor_salEffect
## 1 0.2566239 NA
## 2 0.2709340 NA
## 3 0.3199777 NA
## 4 0.3397117 NA
## 5 0.2064143 NA
## 6 0.2864279 NA
## RDA_absRDAmutpred_cor_tempVa RDA_absRDAmutpred_cor_salVa
## 1 -0.04251831 NA
## 2 -0.06383708 NA
## 3 -0.06156140 NA
## 4 -0.04360391 NA
## 5 -0.05007068 NA
## 6 -0.02697188 NA
## RDA_RDAmutpred_cor_tempEffect_structcorr
## 1 0.1711607
## 2 0.1695575
## 3 0.2590273
## 4 0.1412525
## 5 0.2089774
## 6 0.1483359
## RDA_RDAmutpred_cor_salEffect_structcorr
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## RDA_absRDAmutpred_cor_tempVa_structcorr
## 1 0.002950593
## 2 0.011548773
## 3 -0.006744183
## 4 -0.006811228
## 5 0.018943112
## 6 0.011844784
## RDA_absRDAmutpred_cor_salVa_structcorr RDA_cor_RDA20000temppredict_tempPhen
## 1 NA 0.8530879
## 2 NA 0.8661742
## 3 NA 0.8458619
## 4 NA 0.8566006
## 5 NA 0.8297057
## 6 NA 0.8109670
## RDA_cor_RDA20000salpredict_salPhen
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## RDA_cor_RDA20000temppredict_tempPhen_structcorr
## 1 -0.03541745
## 2 0.02470470
## 3 0.12927728
## 4 0.04149750
## 5 0.11539940
## 6 0.15610811
## RDA_cor_RDA20000salpredict_salPhen_structcorr cor_PC1_temp cor_PC1_sal
## 1 NA -0.9938219 -0.002423473
## 2 NA -0.9763521 -0.002856465
## 3 NA -0.9806495 -0.001193085
## 4 NA -0.9894438 0.006933632
## 5 NA -0.9624753 -0.006417090
## 6 NA -0.9180515 0.333573860
## cor_PC2_temp cor_PC2_sal cor_LFMMU1_temp cor_LFMMU1_sal cor_LFMMU2_temp
## 1 -0.02040717 -0.001688470 0.07027960 -0.001851458 -0.23741491
## 2 0.15699463 0.006663054 -0.64036018 -0.002311619 0.16118062
## 3 0.02289691 -0.002251339 -0.05164944 -0.001922661 -0.66955922
## 4 -0.03159957 -0.011695024 0.09041291 -0.007667182 -0.49064055
## 5 0.18621652 0.005399774 -0.64151191 -0.008355460 0.20361826
## 6 -0.33266762 -0.910485583 0.14489884 -0.108366523 -0.05450648
## cor_LFMMU2_sal cor_PC1_LFMMU1_temp cor_PC1_LFMMU1_sal cor_PC2_LFMMU1_temp
## 1 -0.002823011 -0.09231664 0.9995526 0.9926958
## 2 -0.000199728 0.75497199 0.9963113 0.6504534
## 3 -0.002378429 0.07609171 0.9999081 0.9968609
## 4 0.011008515 -0.12306538 -0.9998272 0.9916547
## 5 -0.005102881 0.79034742 0.9976490 0.5947952
## 6 -0.018196703 -0.47679972 -0.9712646 0.8782899
## cor_PC2_LFMMU1_sal gwas_TPR_sal gwas_TPR_temp gwas_FDR_sal_neutbase
## 1 0.001339894 NA 0.2290323 NA
## 2 0.084015793 NA 0.3272251 NA
## 3 0.004567194 NA 0.3270833 NA
## 4 0.004580758 NA 0.2430704 NA
## 5 0.060281782 NA 0.4202335 NA
## 6 -0.236378957 NA 0.2252747 NA
## gwas_FDR_temp_neutbase clinalparadigm_sal_proptop5GWASclines
## 1 0.9667914 NA
## 2 0.9696749 NA
## 3 0.9616699 NA
## 4 0.9497354 NA
## 5 0.9174312 NA
## 6 0.9382065 NA
## clinalparadigm_temp_proptop5GWASclines clinalparadigm_sal_propsigGWASclines
## 1 0.6554106 NA
## 2 0.5384083 NA
## 3 0.5530760 NA
## 4 0.5021433 NA
## 5 0.5882353 NA
## 6 0.3446602 NA
## clinalparadigm_temp_propsigGWASclines
## 1 0.6050302
## 2 0.5794083
## 3 0.5593769
## 4 0.4777563
## 5 0.6314615
## 6 0.3099063tail(out.df)## seed n_samp_tot n_samp_per_pop sd_fitness_among_inds
## 2245 1233338 1000 10 0.06989773
## 2246 1233339 1000 10 0.10457515
## 2247 1233340 1000 10 0.10815036
## 2248 1233341 1000 10 0.09447908
## 2249 1233342 1000 10 0.10039771
## 2250 1233343 1000 10 0.11933530
## sd_fitness_among_pops final_LA K Bonf_alpha numCausalLowMAFsample
## 2245 0.03432623 0.390202 2 4.302186e-06 0
## 2246 0.05800045 0.378143 3 2.037324e-06 0
## 2247 0.05691502 0.386475 3 1.950687e-06 0
## 2248 0.04351184 0.406991 1 1.582930e-06 0
## 2249 0.04816428 0.411540 2 1.608648e-06 0
## 2250 0.06758560 0.361984 1 6.179706e-06 0
## all_corr_phen_temp subsamp_corr_phen_temp all_corr_phen_sal
## 2245 0.8105442 0.6442488 0.9331842
## 2246 0.6134233 0.5623938 0.8856847
## 2247 0.6300670 0.5378167 0.9092372
## 2248 0.7915280 0.6228879 0.9145244
## 2249 0.8020544 0.6425425 0.9127410
## 2250 0.8163458 0.6347974 0.8849650
## subsamp_corr_phen_sal num_causal_prefilter num_causal_postfilter
## 2245 0.8112850 8 5
## 2246 0.8061503 11 6
## 2247 0.8075816 8 6
## 2248 0.8166540 10 7
## 2249 0.8128461 14 9
## 2250 0.7608153 12 8
## num_non_causal num_neut_prefilter num_neut_postfilter
## 2245 11617 11843 11843
## 2246 24536 25257 25257
## 2247 25626 26975 26975
## 2248 31580 31942 31942
## 2249 31073 31405 31405
## 2250 8083 8197 8197
## num_neut_neutralgenome num_causal_temp num_causal_sal num_multiallelic
## 2245 5810 5 5 0
## 2246 12346 6 6 0
## 2247 12834 6 6 0
## 2248 15946 7 7 0
## 2249 15689 9 9 0
## 2250 3947 8 8 0
## meanFst va_temp_total va_sal_total Va_temp_sample Va_sal_sample nSNPs
## 2245 0.14301783 0.14320895 0.06757681 0.13713509 0.06623635 11622
## 2246 0.13350458 0.05299476 0.07273692 0.08494197 0.09831001 24542
## 2247 0.13740776 0.09929097 0.10670097 0.22910565 0.10650756 25632
## 2248 0.08708807 0.13554966 0.10110684 0.13249490 0.10066003 31587
## 2249 0.06704804 0.16714222 0.16315859 0.16734136 0.16310813 31082
## 2250 0.17379409 0.08556485 0.10592398 0.08049695 0.09935857 8091
## median_causal_temp_cor median_causal_sal_cor median_neut_temp_cor
## 2245 0.2754918 0.5180941 0.2414407
## 2246 0.3026247 0.4175574 0.1850624
## 2247 0.2000535 0.3990216 0.2321665
## 2248 0.2967283 0.4868710 0.2257370
## 2249 0.3428889 0.3406402 0.1621695
## 2250 0.3033895 0.3424647 0.2488890
## median_neut_sal_cor cor_VA_temp_prop cor_VA_sal_prop cor_TPR_temp
## 2245 0.22546761 0.8516887 0.9978557 0.4000000
## 2246 0.12090175 0.4727327 0.8630538 0.1666667
## 2247 0.10810696 0.7985926 0.7317329 0.1666667
## 2248 0.09267506 0.6560965 0.8820640 0.4285714
## 2249 0.10404740 0.1136390 0.4316080 0.3333333
## 2250 0.13587417 0.3901235 0.8777962 0.3750000
## cor_TPR_sal cor_FDR_allSNPs_temp cor_FDR_neutSNPs_temp cor_FDR_allSNPs_sal
## 2245 0.8000000 0.9993947 0.9987358 0.9985337
## 2246 0.6666667 0.9997394 0.9995287 0.9949749
## 2247 0.5000000 0.9998285 0.9996431 0.9933775
## 2248 0.8571429 0.9995787 0.9991545 0.9692308
## 2249 0.3333333 0.9989027 0.9976285 0.9916201
## 2250 0.5000000 0.9987903 0.9974958 0.9913043
## cor_FDR_neutSNPs_sal num_causal_sig_temp_corr num_causal_sig_sal_corr
## 2245 0.9968774 2 4
## 2246 0.9840000 1 4
## 2247 0.9732143 1 3
## 2248 0.8378378 3 6
## 2249 0.9700000 3 3
## 2250 0.9743590 3 4
## num_notCausal_sig_temp_corr num_notCausal_sig_sal_corr
## 2245 3302 2724
## 2246 3836 792
## 2247 5830 450
## 2248 7118 189
## 2249 2731 355
## 2250 2477 456
## num_neut_sig_temp_corr num_neut_sig_sal_corr cor_AUCPR_temp_allSNPs
## 2245 1580 1277 0.0056220144
## 2246 2121 246 0.0007501130
## 2247 2801 109 0.0003226524
## 2248 3545 31 0.0003352525
## 2249 1262 97 0.0050843070
## 2250 1195 152 0.0013870159
## cor_AUCPR_temp_neutSNPs cor_AUCPR_sal_allSNPs cor_AUCPR_sal_neutSNPs
## 2245 0.0161350441 0.09787865 0.4036255
## 2246 0.0013167008 0.24012890 0.3423103
## 2247 0.0006630059 0.17086955 0.1834894
## 2248 0.0006712386 0.40553013 0.7821320
## 2249 0.0109923069 0.11638113 0.1367044
## 2250 0.0029380064 0.02439708 0.2860155
## cor_af_temp_noutliers cor_af_sal_noutliers cor_FPR_temp_neutSNPs
## 2245 3304 2728 0.27194492
## 2246 3837 796 0.17179653
## 2247 5831 453 0.21824840
## 2248 7121 195 0.22231281
## 2249 2734 358 0.08043852
## 2250 2480 460 0.30276159
## cor_FPR_sal_neutSNPs LEA3.2_lfmm2_Va_temp_prop LEA3.2_lfmm2_Va_sal_prop
## 2245 0.219793460 0.0000000 0.9578289
## 2246 0.019925482 0.4727327 0.9998981
## 2247 0.008493065 0.0000000 0.9412170
## 2248 0.001944061 0.0000000 0.8820640
## 2249 0.006182676 0.0000000 0.9171033
## 2250 0.038510261 0.0000000 0.8777962
## LEA3.2_lfmm2_TPR_temp LEA3.2_lfmm2_TPR_sal LEA3.2_lfmm2_FDR_allSNPs_temp
## 2245 0.0000000 0.6000000 NA
## 2246 0.1666667 0.8333333 0
## 2247 0.0000000 0.6666667 NA
## 2248 0.0000000 0.8571429 NA
## 2249 0.0000000 0.5555556 NA
## 2250 0.0000000 0.5000000 NA
## LEA3.2_lfmm2_FDR_allSNPs_sal LEA3.2_lfmm2_FDR_neutSNPs_temp
## 2245 0.9302326 NA
## 2246 0.9180328 0
## 2247 0.9680000 NA
## 2248 0.8888889 NA
## 2249 0.9253731 NA
## 2250 0.9090909 NA
## LEA3.2_lfmm2_FDR_neutSNPs_sal LEA3.2_lfmm2_AUCPR_temp_allSNPs
## 2245 0.0 0.2881630
## 2246 0.0 0.5981415
## 2247 0.2 0.2337020
## 2248 0.0 0.5925314
## 2249 0.0 0.2628556
## 2250 0.0 0.2117759
## LEA3.2_lfmm2_AUCPR_temp_neutSNPs LEA3.2_lfmm2_AUCPR_sal_allSNPs
## 2245 0.6313777 0.2881630
## 2246 0.8340475 0.5981415
## 2247 0.7153403 0.2337020
## 2248 1.0000000 0.5925314
## 2249 0.5658529 0.2628556
## 2250 0.5088647 0.2117759
## LEA3.2_lfmm2_AUCPR_sal_neutSNPs LEA3.2_lfmm2_mlog10P_tempenv_noutliers
## 2245 0.6313777 0
## 2246 0.8340475 1
## 2247 0.7153403 0
## 2248 1.0000000 0
## 2249 0.5658529 0
## 2250 0.5088647 0
## LEA3.2_lfmm2_mlog10P_salenv_noutliers LEA3.2_lfmm2_num_causal_sig_temp
## 2245 43 0
## 2246 61 1
## 2247 125 0
## 2248 54 0
## 2249 67 0
## 2250 44 0
## LEA3.2_lfmm2_num_neut_sig_temp LEA3.2_lfmm2_num_causal_sig_sal
## 2245 0 3
## 2246 0 5
## 2247 0 4
## 2248 0 6
## 2249 0 5
## 2250 0 4
## LEA3.2_lfmm2_num_neut_sig_sal LEA3.2_lfmm2_FPR_neutSNPs_temp
## 2245 0 0
## 2246 0 0
## 2247 1 0
## 2248 0 0
## 2249 0 0
## 2250 0 0
## LEA3.2_lfmm2_FPR_neutSNPs_sal RDA1_propvar RDA2_propvar RDA1_propvar_corr
## 2245 0.000000e+00 0.550 0.450 0.665
## 2246 0.000000e+00 0.742 0.258 0.558
## 2247 7.791803e-05 0.716 0.284 0.772
## 2248 0.000000e+00 0.835 0.165 0.752
## 2249 0.000000e+00 0.684 0.316 0.624
## 2250 0.000000e+00 0.739 0.261 0.673
## RDA2_propvar_corr RDA1_temp_cor RDA1_sal_cor RDA2_temp_cor RDA2_sal_cor
## 2245 0.335 0.5015364 -0.865136554 -0.865136554 -0.5015364
## 2246 0.442 0.9999502 -0.009981000 0.009981000 0.9999502
## 2247 0.228 0.9999683 -0.007965626 0.007965626 0.9999683
## 2248 0.248 0.9991421 0.041413317 -0.041413317 0.9991421
## 2249 0.376 0.9996593 0.026102226 -0.026102226 0.9996593
## 2250 0.327 0.9800515 0.198743495 -0.198743495 0.9800515
## RDA_Va_temp_prop RDA_Va_temp_prop_corr RDA_Va_sal_prop
## 2245 0.4763587 0.8519800 0.9578289
## 2246 0.9996855 0.9996855 0.9998981
## 2247 0.9995510 0.2009584 0.9742365
## 2248 0.9677581 0.9677581 0.8820640
## 2249 0.9861608 0.9861608 0.9986455
## 2250 0.8073374 0.8955353 0.8777962
## RDA_Va_sal_prop_corr RDA_TPR RDA_TPR_corr RDA_FDR_allSNPs
## 2245 0.7328738 0.6000000 0.6000000 0.9931193
## 2246 0.9998981 0.8333333 0.8333333 0.9929478
## 2247 0.9412170 0.8333333 0.6666667 0.9968133
## 2248 0.8820640 0.8571429 0.8571429 0.9963280
## 2249 0.9986455 0.8888889 0.8888889 0.9944328
## 2250 0.9096928 0.5000000 0.6250000 0.9838710
## RDA_FDR_allSNPs_corr num_RDA_sig_causal num_RDA_sig_neutral
## 2245 0.9934211 3 118
## 2246 0.9932249 5 230
## 2247 0.9972318 5 573
## 2248 0.9955720 6 597
## 2249 0.9951190 8 479
## 2250 0.9814815 4 62
## num_RDA_sig_causal_corr num_RDA_sig_neutral_corr RDA_FDR_neutSNPs
## 2245 3 152 0.9752066
## 2246 5 244 0.9787234
## 2247 4 523 0.9913495
## 2248 6 486 0.9900498
## 2249 8 498 0.9835729
## 2250 5 92 0.9393939
## RDA_FDR_neutSNPs_corr RDA_AUCPR_allSNPs RDA_AUCPR_neutSNPs
## 2245 0.9806452 0.1317274 0.6023902
## 2246 0.9799197 0.3896916 0.6725673
## 2247 0.9924099 0.1915153 0.5139129
## 2248 0.9878049 0.3830535 0.8575997
## 2249 0.9841897 0.1615562 0.6298882
## 2250 0.9484536 0.2032180 0.5029961
## RDA_AUCPR_neutSNPs_corr RDA_FPR_neutSNPs RDA_FPR_neutSNPs_corr
## 2245 0.2991643 0.02030981 0.02616179
## 2246 0.6819382 0.01862952 0.01976349
## 2247 0.5028327 0.04464703 0.04075113
## 2248 0.8575851 0.03743886 0.03047786
## 2249 0.6418724 0.03053095 0.03174198
## 2250 0.3320560 0.01570813 0.02330884
## RDA_RDAmutpred_cor_tempEffect RDA_RDAmutpred_cor_salEffect
## 2245 0.6000000 0.6000000
## 2246 0.8666667 0.6000000
## 2247 0.6000000 0.6000000
## 2248 0.5238095 0.3333333
## 2249 0.5555556 0.7777778
## 2250 0.5000000 0.2857143
## RDA_absRDAmutpred_cor_tempVa RDA_absRDAmutpred_cor_salVa
## 2245 0.020465239 0.01871735
## 2246 0.011409116 0.01826038
## 2247 0.002784945 0.01793393
## 2248 0.017384447 0.02012018
## 2249 0.017285637 0.01862984
## 2250 0.020696744 0.02400657
## RDA_RDAmutpred_cor_tempEffect_structcorr
## 2245 0.36606915
## 2246 -0.07001212
## 2247 -0.07228499
## 2248 0.15434486
## 2249 0.12235804
## 2250 0.27521121
## RDA_RDAmutpred_cor_salEffect_structcorr
## 2245 0.9208180
## 2246 0.8150453
## 2247 0.8026655
## 2248 0.7288245
## 2249 0.7696911
## 2250 0.6602084
## RDA_absRDAmutpred_cor_tempVa_structcorr
## 2245 0.04635961
## 2246 0.01113341
## 2247 0.02638952
## 2248 0.06395474
## 2249 0.07991070
## 2250 0.08556552
## RDA_absRDAmutpred_cor_salVa_structcorr
## 2245 0.07122715
## 2246 0.10423641
## 2247 0.09238268
## 2248 0.11222190
## 2249 0.08011436
## 2250 0.11534351
## RDA_cor_RDA20000temppredict_tempPhen RDA_cor_RDA20000salpredict_salPhen
## 2245 0.6025624 0.6970451
## 2246 0.5688492 0.7562831
## 2247 0.5073146 0.6932780
## 2248 0.5917726 0.7525031
## 2249 0.6272143 0.7680113
## 2250 0.5661217 0.6653819
## RDA_cor_RDA20000temppredict_tempPhen_structcorr
## 2245 0.02969630
## 2246 0.08385813
## 2247 0.18613306
## 2248 0.05323003
## 2249 0.12648538
## 2250 0.23854195
## RDA_cor_RDA20000salpredict_salPhen_structcorr cor_PC1_temp cor_PC1_sal
## 2245 0.2459241 -0.49592948 0.707550286
## 2246 0.7608327 -0.03745187 0.010848786
## 2247 0.6778338 -0.42331353 -0.036871726
## 2248 0.7661762 -0.96800660 -0.035807738
## 2249 0.7672155 -0.04223546 0.005524909
## 2250 0.6262283 0.78322231 0.099152034
## cor_PC2_temp cor_PC2_sal cor_LFMMU1_temp cor_LFMMU1_sal cor_LFMMU2_temp
## 2245 -0.79041718 -0.49531715 -0.11538104 -0.196778909 0.41792225
## 2246 -0.93219015 0.01444962 -0.02798592 0.012589562 -0.15180910
## 2247 0.85072814 -0.05730822 -0.07053397 -0.038642435 -0.08318736
## 2248 -0.01224008 0.01880353 -0.01140580 -0.033516495 NA
## 2249 0.95953676 0.03045830 -0.01491703 0.005465646 0.09588969
## 2250 -0.41323121 -0.29453651 0.36840281 -0.066577901 NA
## cor_LFMMU2_sal cor_PC1_LFMMU1_temp cor_PC1_LFMMU1_sal cor_PC2_LFMMU1_temp
## 2245 -0.59810083 0.90723798 -0.7652270 -0.41739868
## 2246 -0.01327245 0.99957697 0.9980697 -0.01001944
## 2247 -0.04185963 0.92380303 0.9857150 0.37884682
## 2248 NA 0.02513634 0.9995552 -0.99878157
## 2249 -0.01555213 0.99870326 -0.9987481 0.02746745
## 2250 NA 0.80908546 -0.9986383 0.58076635
## cor_PC2_LFMMU1_sal gwas_TPR_sal gwas_TPR_temp gwas_FDR_sal_neutbase
## 2245 -6.375626e-01 1.0000000 0.8000000 0.9982047
## 2246 5.599139e-02 1.0000000 0.6666667 0.9990089
## 2247 1.607696e-01 1.0000000 0.3333333 0.9990226
## 2248 -6.278494e-05 1.0000000 0.7142857 0.9987023
## 2249 2.751841e-02 0.8888889 1.0000000 0.9985967
## 2250 -2.590842e-02 0.7500000 0.8750000 0.9975440
## gwas_FDR_temp_neutbase clinalparadigm_sal_proptop5GWASclines
## 2245 0.9944828 0.3144330
## 2246 0.9959225 0.4234528
## 2247 0.9990610 0.3182527
## 2248 0.9943439 0.1227848
## 2249 0.9601770 0.2232947
## 2250 0.9964340 0.5333333
## clinalparadigm_temp_proptop5GWASclines
## 2245 0.2817869
## 2246 0.2060261
## 2247 0.1224649
## 2248 0.2430380
## 2249 0.1273312
## 2250 0.2074074
## clinalparadigm_sal_propsigGWASclines clinalparadigm_temp_propsigGWASclines
## 2245 0.23302128 0.2378011
## 2246 0.06177726 0.1958369
## 2247 0.03441465 0.1351293
## 2248 0.01704694 0.2437673
## 2249 0.02904430 0.1463104
## 2250 0.09349593 0.2850657load("src/0b-final_params-20220428.RData")
sims.df <- final
head(sims.df)## level reps
## 1 highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 1
## 11 highly-polygenic_1-trait__Est-Clines_N-cline-N-to-S_m-constant 1
## 21 highly-polygenic_1-trait__Est-Clines_N-equal_m_breaks 1
## 31 highly-polygenic_1-trait__Est-Clines_N-equal_m-constant 1
## 41 highly-polygenic_1-trait__Est-Clines_N-variable_m-variable 1
## 51 highly-polygenic_1-trait__SS-Clines_N-cline-center-to-edge_m-constant 1
## arch demog_name
## 1 highly-polygenic_1-trait Est-Clines_N-cline-center-to-edge_m-constant
## 11 highly-polygenic_1-trait Est-Clines_N-cline-N-to-S_m-constant
## 21 highly-polygenic_1-trait Est-Clines_N-equal_m_breaks
## 31 highly-polygenic_1-trait Est-Clines_N-equal_m-constant
## 41 highly-polygenic_1-trait Est-Clines_N-variable_m-variable
## 51 highly-polygenic_1-trait SS-Clines_N-cline-center-to-edge_m-constant
## demog_level_sub demog_level MIG_x MIG_y xcline ycline
## 1 N-cline-center-to-edge_m-constant Est-Clines 0.49 0.07 linear linear
## 11 N-cline-N-to-S_m-constant Est-Clines 0.49 0.07 linear linear
## 21 N-equal_m_breaks Est-Clines 0.49 0.07 linear linear
## 31 N-equal_m-constant Est-Clines 0.49 0.07 linear linear
## 41 N-variable_m-variable Est-Clines 0.49 0.07 linear linear
## 51 N-cline-center-to-edge_m-constant SS-Clines 0.03 0.03 linear linear
## demog METAPOP_SIDE_x METAPOP_SIDE_y Nequal isVariableM MIG_breaks
## 1 Estuary 10 10 4 0 0
## 11 Estuary 10 10 2 0 0
## 21 Estuary 10 10 0 0 1
## 31 Estuary 10 10 0 0 0
## 41 Estuary 10 10 3 1 0
## 51 SS 10 10 4 0 0
## arch_level_sub arch_level MU_base MU_QTL_proportion SIGMA_QTN_1
## 1 1-trait highly-polygenic 1e-07 0.25 0.002
## 11 1-trait highly-polygenic 1e-07 0.25 0.002
## 21 1-trait highly-polygenic 1e-07 0.25 0.002
## 31 1-trait highly-polygenic 1e-07 0.25 0.002
## 41 1-trait highly-polygenic 1e-07 0.25 0.002
## 51 1-trait highly-polygenic 1e-07 0.25 0.002
## SIGMA_QTN_2 SIGMA_K_1 SIGMA_K_2 N_traits ispleiotropy seed
## 1 0.002 0.5 0.5 1 0 1231094
## 11 0.002 0.5 0.5 1 0 1231095
## 21 0.002 0.5 0.5 1 0 1231096
## 31 0.002 0.5 0.5 1 0 1231097
## 41 0.002 0.5 0.5 1 0 1231098
## 51 0.002 0.5 0.5 1 0 1231099final.df <- merge(sims.df, out.df, all.x=TRUE)
head(final.df)## seed
## 1 1231094
## 2 1231095
## 3 1231096
## 4 1231097
## 5 1231098
## 6 1231099
## level reps
## 1 highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 1
## 2 highly-polygenic_1-trait__Est-Clines_N-cline-N-to-S_m-constant 1
## 3 highly-polygenic_1-trait__Est-Clines_N-equal_m_breaks 1
## 4 highly-polygenic_1-trait__Est-Clines_N-equal_m-constant 1
## 5 highly-polygenic_1-trait__Est-Clines_N-variable_m-variable 1
## 6 highly-polygenic_1-trait__SS-Clines_N-cline-center-to-edge_m-constant 1
## arch demog_name
## 1 highly-polygenic_1-trait Est-Clines_N-cline-center-to-edge_m-constant
## 2 highly-polygenic_1-trait Est-Clines_N-cline-N-to-S_m-constant
## 3 highly-polygenic_1-trait Est-Clines_N-equal_m_breaks
## 4 highly-polygenic_1-trait Est-Clines_N-equal_m-constant
## 5 highly-polygenic_1-trait Est-Clines_N-variable_m-variable
## 6 highly-polygenic_1-trait SS-Clines_N-cline-center-to-edge_m-constant
## demog_level_sub demog_level MIG_x MIG_y xcline ycline
## 1 N-cline-center-to-edge_m-constant Est-Clines 0.49 0.07 linear linear
## 2 N-cline-N-to-S_m-constant Est-Clines 0.49 0.07 linear linear
## 3 N-equal_m_breaks Est-Clines 0.49 0.07 linear linear
## 4 N-equal_m-constant Est-Clines 0.49 0.07 linear linear
## 5 N-variable_m-variable Est-Clines 0.49 0.07 linear linear
## 6 N-cline-center-to-edge_m-constant SS-Clines 0.03 0.03 linear linear
## demog METAPOP_SIDE_x METAPOP_SIDE_y Nequal isVariableM MIG_breaks
## 1 Estuary 10 10 4 0 0
## 2 Estuary 10 10 2 0 0
## 3 Estuary 10 10 0 0 1
## 4 Estuary 10 10 0 0 0
## 5 Estuary 10 10 3 1 0
## 6 SS 10 10 4 0 0
## arch_level_sub arch_level MU_base MU_QTL_proportion SIGMA_QTN_1
## 1 1-trait highly-polygenic 1e-07 0.25 0.002
## 2 1-trait highly-polygenic 1e-07 0.25 0.002
## 3 1-trait highly-polygenic 1e-07 0.25 0.002
## 4 1-trait highly-polygenic 1e-07 0.25 0.002
## 5 1-trait highly-polygenic 1e-07 0.25 0.002
## 6 1-trait highly-polygenic 1e-07 0.25 0.002
## SIGMA_QTN_2 SIGMA_K_1 SIGMA_K_2 N_traits ispleiotropy n_samp_tot
## 1 0.002 0.5 0.5 1 0 1000
## 2 0.002 0.5 0.5 1 0 1000
## 3 0.002 0.5 0.5 1 0 1000
## 4 0.002 0.5 0.5 1 0 1000
## 5 0.002 0.5 0.5 1 0 1000
## 6 0.002 0.5 0.5 1 0 1000
## n_samp_per_pop sd_fitness_among_inds sd_fitness_among_pops final_LA K
## 1 10 0.05272602 0.01977273 0.500854 2
## 2 10 0.04519923 0.01541145 0.505075 9
## 3 10 0.05348903 0.02022721 0.499265 9
## 4 10 0.05446050 0.01731323 0.499365 9
## 5 10 0.05093029 0.01832588 0.501054 6
## 6 10 0.07552882 0.02341833 0.478131 3
## Bonf_alpha numCausalLowMAFsample all_corr_phen_temp subsamp_corr_phen_temp
## 1 1.918428e-06 39 0.9545790 0.8893387
## 2 1.730523e-06 104 0.9545764 0.9006961
## 3 1.508614e-06 22 0.9723022 0.8873320
## 4 1.531065e-06 24 0.9720233 0.8895298
## 5 6.395498e-06 12 0.9708565 0.8921410
## 6 2.022981e-06 34 0.9247450 0.8525464
## all_corr_phen_sal subsamp_corr_phen_sal num_causal_prefilter
## 1 NA NA 2628
## 2 NA NA 3144
## 3 NA NA 3149
## 4 NA NA 3131
## 5 NA NA 1823
## 6 NA NA 2948
## num_causal_postfilter num_non_causal num_neut_prefilter num_neut_postfilter
## 1 310 25753 26587 26587
## 2 382 28511 30284 30284
## 3 482 32661 33031 33031
## 4 470 32187 32548 32548
## 5 257 7561 7753 7753
## 6 364 24352 25004 25004
## num_neut_neutralgenome num_causal_temp num_causal_sal num_multiallelic
## 1 12867 310 0 0
## 2 14060 382 0 0
## 3 16379 480 0 0
## 4 16243 469 0 0
## 5 3749 257 0 0
## 6 12197 364 0 0
## meanFst va_temp_total va_sal_total Va_temp_sample Va_sal_sample nSNPs
## 1 0.18039488 0.01185008 0 0.01356519 0 26063
## 2 0.16985797 0.01343736 0 0.01276814 0 28893
## 3 0.13396101 0.01405264 0 0.01370501 0 33143
## 4 0.08714633 0.01337458 0 0.01305077 0 32657
## 5 0.31813358 0.01605415 0 0.01622973 0 7818
## 6 0.12616527 0.01930127 0 0.02072472 0 24716
## median_causal_temp_cor median_causal_sal_cor median_neut_temp_cor
## 1 0.3580437 NA 0.3820531
## 2 0.4138162 NA 0.4536498
## 3 0.4157033 NA 0.4029319
## 4 0.4112619 NA 0.3451512
## 5 0.3925022 NA 0.4432208
## 6 0.2974287 NA 0.2243095
## median_neut_sal_cor cor_VA_temp_prop cor_VA_sal_prop cor_TPR_temp cor_TPR_sal
## 1 0.04816897 0.8451605 0 0.4612903 NA
## 2 0.04175877 0.7846421 0 0.5314136 NA
## 3 0.03844065 0.7830061 0 0.5416667 NA
## 4 0.04529330 0.7631297 0 0.5351812 NA
## 5 0.05141380 0.8171677 0 0.5408560 NA
## 6 0.19531471 0.6459740 0 0.3076923 NA
## cor_FDR_allSNPs_temp cor_FDR_neutSNPs_temp cor_FDR_allSNPs_sal
## 1 0.9894449 0.9791241 NA
## 2 0.9886134 0.9769893 NA
## 3 0.9854586 0.9711015 NA
## 4 0.9833466 0.9669693 1
## 5 0.9711199 0.9447316 NA
## 6 0.9813892 0.9575114 1
## cor_FDR_neutSNPs_sal num_causal_sig_temp_corr num_causal_sig_sal_corr
## 1 NA 143 0
## 2 NA 203 0
## 3 NA 260 0
## 4 NA 251 0
## 5 NA 139 0
## 6 1 112 0
## num_notCausal_sig_temp_corr num_notCausal_sig_sal_corr num_neut_sig_temp_corr
## 1 13405 0 6707
## 2 17625 0 8619
## 3 17620 0 8737
## 4 14821 1 7348
## 5 4674 0 2376
## 6 5906 4344 2524
## num_neut_sig_sal_corr cor_AUCPR_temp_allSNPs cor_AUCPR_temp_neutSNPs
## 1 0 0.01064045 0.02148047
## 2 0 0.01053868 0.02130552
## 3 0 0.01286086 0.02546132
## 4 0 0.01566591 0.03219810
## 5 0 0.02696219 0.05312152
## 6 2133 0.02255358 0.06178401
## cor_AUCPR_sal_allSNPs cor_AUCPR_sal_neutSNPs cor_af_temp_noutliers
## 1 NA NA 13548
## 2 NA NA 17828
## 3 NA NA 17880
## 4 NA NA 15072
## 5 NA NA 4813
## 6 NA NA 6018
## cor_af_sal_noutliers cor_FPR_temp_neutSNPs cor_FPR_sal_neutSNPs
## 1 0 0.5212559 0.0000000
## 2 0 0.6130156 0.0000000
## 3 0 0.5334269 0.0000000
## 4 1 0.4523795 0.0000000
## 5 0 0.6337690 0.0000000
## 6 4344 0.2069361 0.1748791
## LEA3.2_lfmm2_Va_temp_prop LEA3.2_lfmm2_Va_sal_prop LEA3.2_lfmm2_TPR_temp
## 1 0.27826502 NA 0.022580645
## 2 0.00000000 NA 0.000000000
## 3 0.00000000 NA 0.000000000
## 4 0.00000000 NA 0.000000000
## 5 0.03610723 NA 0.003891051
## 6 0.17163124 NA 0.005494505
## LEA3.2_lfmm2_TPR_sal LEA3.2_lfmm2_FDR_allSNPs_temp
## 1 NA 0.9789790
## 2 NA NA
## 3 NA NA
## 4 NA 1.0000000
## 5 NA 0.9500000
## 6 NA 0.8666667
## LEA3.2_lfmm2_FDR_allSNPs_sal LEA3.2_lfmm2_FDR_neutSNPs_temp
## 1 NA 0.9263158
## 2 1 NA
## 3 1 NA
## 4 NA 1.0000000
## 5 NA 0.8750000
## 6 NA 0.0000000
## LEA3.2_lfmm2_FDR_neutSNPs_sal LEA3.2_lfmm2_AUCPR_temp_allSNPs
## 1 NA 0.01339495
## 2 NA 0.01367013
## 3 NA 0.01426730
## 4 NA 0.01310529
## 5 NA 0.03463172
## 6 NA 0.01437720
## LEA3.2_lfmm2_AUCPR_temp_neutSNPs LEA3.2_lfmm2_AUCPR_sal_allSNPs
## 1 0.02655237 NA
## 2 0.02786249 NA
## 3 0.02851768 NA
## 4 0.02513767 NA
## 5 0.06672248 NA
## 6 0.02928224 NA
## LEA3.2_lfmm2_AUCPR_sal_neutSNPs LEA3.2_lfmm2_mlog10P_tempenv_noutliers
## 1 NA 333
## 2 NA 0
## 3 NA 0
## 4 NA 6
## 5 NA 20
## 6 NA 15
## LEA3.2_lfmm2_mlog10P_salenv_noutliers LEA3.2_lfmm2_num_causal_sig_temp
## 1 0 7
## 2 1 0
## 3 1 0
## 4 0 0
## 5 0 1
## 6 0 2
## LEA3.2_lfmm2_num_neut_sig_temp LEA3.2_lfmm2_num_causal_sig_sal
## 1 88 0
## 2 0 0
## 3 0 0
## 4 2 0
## 5 7 0
## 6 0 0
## LEA3.2_lfmm2_num_neut_sig_sal LEA3.2_lfmm2_FPR_neutSNPs_temp
## 1 0 0.006839201
## 2 0 0.000000000
## 3 0 0.000000000
## 4 0 0.000123130
## 5 0 0.001867165
## 6 0 0.000000000
## LEA3.2_lfmm2_FPR_neutSNPs_sal RDA1_propvar RDA2_propvar RDA1_propvar_corr
## 1 0 0.985 0.015 0.765
## 2 0 0.989 0.011 0.835
## 3 0 0.988 0.012 0.842
## 4 0 0.980 0.020 0.841
## 5 0 0.991 0.009 0.816
## 6 0 0.596 0.404 0.597
## RDA2_propvar_corr RDA1_temp_cor RDA1_sal_cor RDA2_temp_cor RDA2_sal_cor
## 1 0.235 0.9999982 0.001912722 -0.001912722 0.9999982
## 2 0.165 0.9999971 0.002425692 -0.002425692 0.9999971
## 3 0.158 0.9999982 0.001888450 -0.001888450 0.9999982
## 4 0.159 0.9999700 -0.007739672 0.007739672 0.9999700
## 5 0.184 0.9999640 0.008484251 -0.008484251 0.9999640
## 6 0.403 -0.9360957 0.351745296 -0.351745296 -0.9360957
## RDA_Va_temp_prop RDA_Va_temp_prop_corr RDA_Va_sal_prop RDA_Va_sal_prop_corr
## 1 0.1248244 0.000000000 0 0
## 2 0.3566111 0.017427370 0 0
## 3 0.1750594 0.000000000 0 0
## 4 0.1339135 0.000000000 0 0
## 5 0.3123098 0.036329553 0 0
## 6 0.3414487 0.004385126 0 0
## RDA_TPR RDA_TPR_corr RDA_FDR_allSNPs RDA_FDR_allSNPs_corr
## 1 0.006451613 0.000000000 0.9918699 1.0000000
## 2 0.041884817 0.015706806 0.9953502 0.9966979
## 3 0.010373444 0.000000000 0.9957519 1.0000000
## 4 0.014893617 0.000000000 0.9941812 1.0000000
## 5 0.073929961 0.015564202 0.9850039 0.9833333
## 6 0.027472527 0.002747253 0.9889258 0.9979550
## num_RDA_sig_causal num_RDA_sig_neutral num_RDA_sig_causal_corr
## 1 2 118 0
## 2 16 1545 6
## 3 5 526 0
## 4 7 557 0
## 5 19 583 4
## 6 10 271 1
## num_RDA_sig_neutral_corr RDA_FDR_neutSNPs RDA_FDR_neutSNPs_corr
## 1 200 0.9833333 1.0000000
## 2 867 0.9897502 0.9931271
## 3 458 0.9905838 1.0000000
## 4 577 0.9875887 1.0000000
## 5 122 0.9684385 0.9682540
## 6 213 0.9644128 0.9953271
## RDA_AUCPR_allSNPs RDA_AUCPR_neutSNPs RDA_AUCPR_neutSNPs_corr RDA_FPR_neutSNPs
## 1 0.008249728 0.01647883 0.01597697 0.009170747
## 2 0.008916531 0.01857113 0.01824752 0.109886202
## 3 0.009025124 0.01784801 0.01820267 0.032114293
## 4 0.009215389 0.01812750 0.01825734 0.034291695
## 5 0.025358473 0.05094037 0.05046366 0.155508136
## 6 0.014302424 0.02930863 0.01982718 0.022218578
## RDA_FPR_neutSNPs_corr RDA_RDAmutpred_cor_tempEffect
## 1 0.01554364 0.2566239
## 2 0.06166430 0.2709340
## 3 0.02796264 0.3199777
## 4 0.03552299 0.3397117
## 5 0.03254201 0.2064143
## 6 0.01746331 0.2864279
## RDA_RDAmutpred_cor_salEffect RDA_absRDAmutpred_cor_tempVa
## 1 NA -0.04251831
## 2 NA -0.06383708
## 3 NA -0.06156140
## 4 NA -0.04360391
## 5 NA -0.05007068
## 6 NA -0.02697188
## RDA_absRDAmutpred_cor_salVa RDA_RDAmutpred_cor_tempEffect_structcorr
## 1 NA 0.1711607
## 2 NA 0.1695575
## 3 NA 0.2590273
## 4 NA 0.1412525
## 5 NA 0.2089774
## 6 NA 0.1483359
## RDA_RDAmutpred_cor_salEffect_structcorr
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## RDA_absRDAmutpred_cor_tempVa_structcorr
## 1 0.002950593
## 2 0.011548773
## 3 -0.006744183
## 4 -0.006811228
## 5 0.018943112
## 6 0.011844784
## RDA_absRDAmutpred_cor_salVa_structcorr RDA_cor_RDA20000temppredict_tempPhen
## 1 NA 0.8530879
## 2 NA 0.8661742
## 3 NA 0.8458619
## 4 NA 0.8566006
## 5 NA 0.8297057
## 6 NA 0.8109670
## RDA_cor_RDA20000salpredict_salPhen
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## RDA_cor_RDA20000temppredict_tempPhen_structcorr
## 1 -0.03541745
## 2 0.02470470
## 3 0.12927728
## 4 0.04149750
## 5 0.11539940
## 6 0.15610811
## RDA_cor_RDA20000salpredict_salPhen_structcorr cor_PC1_temp cor_PC1_sal
## 1 NA -0.9938219 -0.002423473
## 2 NA -0.9763521 -0.002856465
## 3 NA -0.9806495 -0.001193085
## 4 NA -0.9894438 0.006933632
## 5 NA -0.9624753 -0.006417090
## 6 NA -0.9180515 0.333573860
## cor_PC2_temp cor_PC2_sal cor_LFMMU1_temp cor_LFMMU1_sal cor_LFMMU2_temp
## 1 -0.02040717 -0.001688470 0.07027960 -0.001851458 -0.23741491
## 2 0.15699463 0.006663054 -0.64036018 -0.002311619 0.16118062
## 3 0.02289691 -0.002251339 -0.05164944 -0.001922661 -0.66955922
## 4 -0.03159957 -0.011695024 0.09041291 -0.007667182 -0.49064055
## 5 0.18621652 0.005399774 -0.64151191 -0.008355460 0.20361826
## 6 -0.33266762 -0.910485583 0.14489884 -0.108366523 -0.05450648
## cor_LFMMU2_sal cor_PC1_LFMMU1_temp cor_PC1_LFMMU1_sal cor_PC2_LFMMU1_temp
## 1 -0.002823011 -0.09231664 0.9995526 0.9926958
## 2 -0.000199728 0.75497199 0.9963113 0.6504534
## 3 -0.002378429 0.07609171 0.9999081 0.9968609
## 4 0.011008515 -0.12306538 -0.9998272 0.9916547
## 5 -0.005102881 0.79034742 0.9976490 0.5947952
## 6 -0.018196703 -0.47679972 -0.9712646 0.8782899
## cor_PC2_LFMMU1_sal gwas_TPR_sal gwas_TPR_temp gwas_FDR_sal_neutbase
## 1 0.001339894 NA 0.2290323 NA
## 2 0.084015793 NA 0.3272251 NA
## 3 0.004567194 NA 0.3270833 NA
## 4 0.004580758 NA 0.2430704 NA
## 5 0.060281782 NA 0.4202335 NA
## 6 -0.236378957 NA 0.2252747 NA
## gwas_FDR_temp_neutbase clinalparadigm_sal_proptop5GWASclines
## 1 0.9667914 NA
## 2 0.9696749 NA
## 3 0.9616699 NA
## 4 0.9497354 NA
## 5 0.9174312 NA
## 6 0.9382065 NA
## clinalparadigm_temp_proptop5GWASclines clinalparadigm_sal_propsigGWASclines
## 1 0.6554106 NA
## 2 0.5384083 NA
## 3 0.5530760 NA
## 4 0.5021433 NA
## 5 0.5882353 NA
## 6 0.3446602 NA
## clinalparadigm_temp_propsigGWASclines
## 1 0.6050302
## 2 0.5794083
## 3 0.5593769
## 4 0.4777563
## 5 0.6314615
## 6 0.3099063(vars <- t(final.df[1,]))## 1
## seed "1231094"
## level "highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant"
## reps "1"
## arch "highly-polygenic_1-trait"
## demog_name "Est-Clines_N-cline-center-to-edge_m-constant"
## demog_level_sub "N-cline-center-to-edge_m-constant"
## demog_level "Est-Clines"
## MIG_x "0.49"
## MIG_y "0.07"
## xcline "linear"
## ycline "linear"
## demog "Estuary"
## METAPOP_SIDE_x "10"
## METAPOP_SIDE_y "10"
## Nequal "4"
## isVariableM "0"
## MIG_breaks "0"
## arch_level_sub "1-trait"
## arch_level "highly-polygenic"
## MU_base "1e-07"
## MU_QTL_proportion "0.25"
## SIGMA_QTN_1 "0.002"
## SIGMA_QTN_2 "0.002"
## SIGMA_K_1 "0.5"
## SIGMA_K_2 "0.5"
## N_traits "1"
## ispleiotropy "0"
## n_samp_tot "1000"
## n_samp_per_pop "10"
## sd_fitness_among_inds "0.05272602"
## sd_fitness_among_pops "0.01977273"
## final_LA "0.500854"
## K "2"
## Bonf_alpha "1.918428e-06"
## numCausalLowMAFsample "39"
## all_corr_phen_temp "0.954579"
## subsamp_corr_phen_temp "0.8893387"
## all_corr_phen_sal NA
## subsamp_corr_phen_sal NA
## num_causal_prefilter "2628"
## num_causal_postfilter "310"
## num_non_causal "25753"
## num_neut_prefilter "26587"
## num_neut_postfilter "26587"
## num_neut_neutralgenome "12867"
## num_causal_temp "310"
## num_causal_sal "0"
## num_multiallelic "0"
## meanFst "0.1803949"
## va_temp_total "0.01185008"
## va_sal_total "0"
## Va_temp_sample "0.01356519"
## Va_sal_sample "0"
## nSNPs "26063"
## median_causal_temp_cor "0.3580437"
## median_causal_sal_cor NA
## median_neut_temp_cor "0.3820531"
## median_neut_sal_cor "0.04816897"
## cor_VA_temp_prop "0.8451605"
## cor_VA_sal_prop "0"
## cor_TPR_temp "0.4612903"
## cor_TPR_sal NA
## cor_FDR_allSNPs_temp "0.9894449"
## cor_FDR_neutSNPs_temp "0.9791241"
## cor_FDR_allSNPs_sal NA
## cor_FDR_neutSNPs_sal NA
## num_causal_sig_temp_corr "143"
## num_causal_sig_sal_corr "0"
## num_notCausal_sig_temp_corr "13405"
## num_notCausal_sig_sal_corr "0"
## num_neut_sig_temp_corr "6707"
## num_neut_sig_sal_corr "0"
## cor_AUCPR_temp_allSNPs "0.01064045"
## cor_AUCPR_temp_neutSNPs "0.02148047"
## cor_AUCPR_sal_allSNPs NA
## cor_AUCPR_sal_neutSNPs NA
## cor_af_temp_noutliers "13548"
## cor_af_sal_noutliers "0"
## cor_FPR_temp_neutSNPs "0.5212559"
## cor_FPR_sal_neutSNPs "0"
## LEA3.2_lfmm2_Va_temp_prop "0.278265"
## LEA3.2_lfmm2_Va_sal_prop NA
## LEA3.2_lfmm2_TPR_temp "0.02258065"
## LEA3.2_lfmm2_TPR_sal NA
## LEA3.2_lfmm2_FDR_allSNPs_temp "0.978979"
## LEA3.2_lfmm2_FDR_allSNPs_sal NA
## LEA3.2_lfmm2_FDR_neutSNPs_temp "0.9263158"
## LEA3.2_lfmm2_FDR_neutSNPs_sal NA
## LEA3.2_lfmm2_AUCPR_temp_allSNPs "0.01339495"
## LEA3.2_lfmm2_AUCPR_temp_neutSNPs "0.02655237"
## LEA3.2_lfmm2_AUCPR_sal_allSNPs NA
## LEA3.2_lfmm2_AUCPR_sal_neutSNPs NA
## LEA3.2_lfmm2_mlog10P_tempenv_noutliers "333"
## LEA3.2_lfmm2_mlog10P_salenv_noutliers "0"
## LEA3.2_lfmm2_num_causal_sig_temp "7"
## LEA3.2_lfmm2_num_neut_sig_temp "88"
## LEA3.2_lfmm2_num_causal_sig_sal "0"
## LEA3.2_lfmm2_num_neut_sig_sal "0"
## LEA3.2_lfmm2_FPR_neutSNPs_temp "0.006839201"
## LEA3.2_lfmm2_FPR_neutSNPs_sal "0"
## RDA1_propvar "0.985"
## RDA2_propvar "0.015"
## RDA1_propvar_corr "0.765"
## RDA2_propvar_corr "0.235"
## RDA1_temp_cor "0.9999982"
## RDA1_sal_cor "0.001912722"
## RDA2_temp_cor "-0.001912722"
## RDA2_sal_cor "0.9999982"
## RDA_Va_temp_prop "0.1248244"
## RDA_Va_temp_prop_corr "0"
## RDA_Va_sal_prop "0"
## RDA_Va_sal_prop_corr "0"
## RDA_TPR "0.006451613"
## RDA_TPR_corr "0"
## RDA_FDR_allSNPs "0.9918699"
## RDA_FDR_allSNPs_corr "1"
## num_RDA_sig_causal "2"
## num_RDA_sig_neutral "118"
## num_RDA_sig_causal_corr "0"
## num_RDA_sig_neutral_corr "200"
## RDA_FDR_neutSNPs "0.9833333"
## RDA_FDR_neutSNPs_corr "1"
## RDA_AUCPR_allSNPs "0.008249728"
## RDA_AUCPR_neutSNPs "0.01647883"
## RDA_AUCPR_neutSNPs_corr "0.01597697"
## RDA_FPR_neutSNPs "0.009170747"
## RDA_FPR_neutSNPs_corr "0.01554364"
## RDA_RDAmutpred_cor_tempEffect "0.2566239"
## RDA_RDAmutpred_cor_salEffect NA
## RDA_absRDAmutpred_cor_tempVa "-0.04251831"
## RDA_absRDAmutpred_cor_salVa NA
## RDA_RDAmutpred_cor_tempEffect_structcorr "0.1711607"
## RDA_RDAmutpred_cor_salEffect_structcorr NA
## RDA_absRDAmutpred_cor_tempVa_structcorr "0.002950593"
## RDA_absRDAmutpred_cor_salVa_structcorr NA
## RDA_cor_RDA20000temppredict_tempPhen "0.8530879"
## RDA_cor_RDA20000salpredict_salPhen NA
## RDA_cor_RDA20000temppredict_tempPhen_structcorr "-0.03541745"
## RDA_cor_RDA20000salpredict_salPhen_structcorr NA
## cor_PC1_temp "-0.9938219"
## cor_PC1_sal "-0.002423473"
## cor_PC2_temp "-0.02040717"
## cor_PC2_sal "-0.00168847"
## cor_LFMMU1_temp "0.0702796"
## cor_LFMMU1_sal "-0.001851458"
## cor_LFMMU2_temp "-0.2374149"
## cor_LFMMU2_sal "-0.002823011"
## cor_PC1_LFMMU1_temp "-0.09231664"
## cor_PC1_LFMMU1_sal "0.9995526"
## cor_PC2_LFMMU1_temp "0.9926958"
## cor_PC2_LFMMU1_sal "0.001339894"
## gwas_TPR_sal NA
## gwas_TPR_temp "0.2290323"
## gwas_FDR_sal_neutbase NA
## gwas_FDR_temp_neutbase "0.9667914"
## clinalparadigm_sal_proptop5GWASclines NA
## clinalparadigm_temp_proptop5GWASclines "0.6554106"
## clinalparadigm_sal_propsigGWASclines NA
## clinalparadigm_temp_propsigGWASclines "0.6050302"ref <- final.df[,c("seed", "level", "num_causal_postfilter")]
### Make sure 10 reps of each simulation
count <- data.frame(notNA=tapply(final.df$K, final.df$level,
function(x){sum(!is.na(x))}
))
count## notNA
## highly-polygenic_1-trait__SS-Mtn_N-equal_m-constant 10
## highly-polygenic_1-trait__SS-Mtn_N-variable_m-variable 10
## highly-polygenic_1-trait__Est-Clines_N-equal_m_breaks 10
## highly-polygenic_1-trait__Est-Clines_N-equal_m-constant 10
## highly-polygenic_1-trait__SS-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_1-trait__SS-Clines_N-equal_m-constant 10
## highly-polygenic_1-trait__SS-Mtn_N-cline-center-to-edge_m-constant 10
## highly-polygenic_1-trait__SS-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_1-trait__Est-Clines_N-variable_m-variable 10
## highly-polygenic_1-trait__SS-Clines_N-equal_m_breaks 10
## highly-polygenic_1-trait__SS-Clines_N-variable_m-variable 10
## highly-polygenic_1-trait__SS-Mtn_N-equal_m_breaks 10
## highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_1-trait__Est-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_1-trait__SS-Mtn_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-equal_m_breaks 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-equal_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-variable_m-variable 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-variable_m-variable 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-equal_m_breaks 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-equal_m_breaks 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-equal_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-variable_m-variable 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-equal_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-equal_m_breaks 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-equal_m_breaks 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-variable_m-variable 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-equal_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-variable_m-variable 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-equal_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-equal_m_breaks 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-equal_m-constant 10
## highly-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-variable_m-variable 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-equal_m_breaks 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-equal_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-equal_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-variable_m-variable 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-equal_m_breaks 10
## highly-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-equal_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-equal_m_breaks 10
## highly-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-variable_m-variable 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-equal_m-constant 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-equal_m-constant 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-equal_m-constant 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-variable_m-variable 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-equal_m_breaks 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-variable_m-variable 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-equal_m_breaks 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## highly-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-equal_m_breaks 10
## mod-polygenic_1-trait__SS-Mtn_N-equal_m_breaks 10
## mod-polygenic_1-trait__SS-Mtn_N-variable_m-variable 10
## mod-polygenic_1-trait__SS-Mtn_N-equal_m-constant 10
## mod-polygenic_1-trait__SS-Mtn_N-cline-center-to-edge_m-constant 10
## mod-polygenic_1-trait__SS-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_1-trait__Est-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_1-trait__SS-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_1-trait__SS-Clines_N-equal_m-constant 10
## mod-polygenic_1-trait__Est-Clines_N-equal_m_breaks 10
## mod-polygenic_1-trait__SS-Clines_N-equal_m_breaks 10
## mod-polygenic_1-trait__Est-Clines_N-variable_m-variable 10
## mod-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_1-trait__SS-Clines_N-variable_m-variable 10
## mod-polygenic_1-trait__SS-Mtn_N-cline-N-to-S_m-constant 10
## mod-polygenic_1-trait__Est-Clines_N-equal_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-variable_m-variable 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-equal_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-equal_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-equal_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-equal_m_breaks 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-equal_m_breaks 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-variable_m-variable 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-variable_m-variable 10
## mod-polygenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-equal_m_breaks 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-equal_m_breaks 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-equal_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-variable_m-variable 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-equal_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-equal_m_breaks 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-equal_m-constant 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-variable_m-variable 10
## mod-polygenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-equal_m_breaks 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-equal_m_breaks 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-equal_m-constant 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-equal_m_breaks 10
## mod-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-variable_m-variable 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-variable_m-variable 10
## mod-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-equal_m-constant 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-equal_m-constant 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-pleiotropy-equal-S__Est-Clines_N-variable_m-variable 10
## mod-polygenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-equal_m_breaks 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-equal_m-constant 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-equal_m_breaks 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-equal_m_breaks 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-equal_m_breaks 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-variable_m-variable 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-variable_m-variable 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-equal_m-constant 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## mod-polygenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-equal_m-constant 10
## oliogenic_1-trait__SS-Clines_N-equal_m-constant 10
## oliogenic_1-trait__SS-Mtn_N-cline-N-to-S_m-constant 10
## oliogenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_1-trait__Est-Clines_N-variable_m-variable 10
## oliogenic_1-trait__Est-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_1-trait__SS-Mtn_N-cline-center-to-edge_m-constant 10
## oliogenic_1-trait__Est-Clines_N-equal_m_breaks 10
## oliogenic_1-trait__Est-Clines_N-equal_m-constant 10
## oliogenic_1-trait__SS-Clines_N-equal_m_breaks 10
## oliogenic_1-trait__SS-Mtn_N-variable_m-variable 10
## oliogenic_1-trait__SS-Mtn_N-equal_m_breaks 10
## oliogenic_1-trait__SS-Clines_N-variable_m-variable 10
## oliogenic_1-trait__SS-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_1-trait__SS-Mtn_N-equal_m-constant 10
## oliogenic_1-trait__SS-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-variable_m-variable 10
## oliogenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-equal_m-constant 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-equal_m-constant 10
## oliogenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-equal_m_breaks 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-equal_m_breaks 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Mtn_N-variable_m-variable 10
## oliogenic_2-trait-no-pleiotropy-equal-S__Est-Clines_N-variable_m-variable 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-equal_m_breaks 10
## oliogenic_2-trait-no-pleiotropy-equal-S__SS-Clines_N-equal_m-constant 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-equal_m-constant 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-equal_m-constant 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-equal_m_breaks 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-variable_m-variable 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-equal_m-constant 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Clines_N-variable_m-variable 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-equal_m_breaks 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__Est-Clines_N-equal_m_breaks 10
## oliogenic_2-trait-no-pleiotropy-unequal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-pleiotropy-equal-S__Est-Clines_N-equal_m-constant 10
## oliogenic_2-trait-pleiotropy-equal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Clines_N-equal_m-constant 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-equal_m_breaks 10
## oliogenic_2-trait-pleiotropy-equal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-equal_m-constant 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Clines_N-equal_m_breaks 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-variable_m-variable 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Clines_N-variable_m-variable 10
## oliogenic_2-trait-pleiotropy-equal-S__Est-Clines_N-variable_m-variable 10
## oliogenic_2-trait-pleiotropy-equal-S__Est-Clines_N-equal_m_breaks 10
## oliogenic_2-trait-pleiotropy-equal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-variable_m-variable 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-equal_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-equal_m_breaks 10
## oliogenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-equal_m_breaks 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-equal_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-equal_m_breaks 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-cline-N-to-S_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-equal_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__Est-Clines_N-cline-center-to-edge_m-constant 10
## oliogenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-variable_m-variable 10### Make sure no NAs in population structure
sum(is.na(final.df$K))## [1] 0# Check the levels
levels(final.df$demog_level_sub)## [1] "N-equal_m-constant" "N-cline-N-to-S_m-constant"
## [3] "N-cline-center-to-edge_m-constant" "N-equal_m_breaks"
## [5] "N-variable_m-variable"levels(final.df$arch_level_sub)## [1] "1-trait" "2-trait-no-pleiotropy-equal-S"
## [3] "2-trait-no-pleiotropy-unequal-S" "2-trait-pleiotropy-equal-S"
## [5] "2-trait-pleiotropy-unequal-S"Copy graphs
This optional code is for copying figures for one replicate of each of the 225 levels.
GGtheme for all figures
ggtheme <- theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), panel.border=element_blank(), axis.line = element_line(colour="grey30"), axis.title = element_text(colour="grey20"), axis.text = (element_text(colour="grey30")), legend.title = element_text(colour="grey20"), legend.text = element_text(colour="grey30"))Color schemes
viridis For SS, SS-Mtn, Estuary mako for demography sub level (m and N) magma for arch_level_sub rocket for arch_level scale_color_viridis(name=“Genic level”, discrete=TRUE, option=“rocket”, begin=0, end=0.9)
Reorder levels
str(final.df$arch_level)## Ord.factor w/ 3 levels "highly-polygenic"<..: 1 1 1 1 1 1 1 1 1 1 ...levels(final.df$arch_level)## [1] "highly-polygenic" "mod-polygenic" "oliogenic"final.df$arch_level <- factor(final.df$arch_level, levels=c( "oliogenic", "mod-polygenic", "highly-polygenic"), ordered=TRUE)
levels(final.df$arch_level) <- c("oligogenic", "mod.\npolygenic", "highly\npolygenic")
str(final.df$arch_level) ## Ord.factor w/ 3 levels "oligogenic"<"mod.\npolygenic"<..: 3 3 3 3 3 3 3 3 3 3 ...levels(final.df$arch_level_sub)## [1] "1-trait" "2-trait-no-pleiotropy-equal-S"
## [3] "2-trait-no-pleiotropy-unequal-S" "2-trait-pleiotropy-equal-S"
## [5] "2-trait-pleiotropy-unequal-S"final.df$ispleiotropy <- factor(final.df$ispleiotropy)
levels(final.df$ispleiotropy) <- c("No pleiotropy", "Pleiotropy")
head(final.df$ispleiotropy)## [1] No pleiotropy No pleiotropy No pleiotropy No pleiotropy No pleiotropy
## [6] No pleiotropy
## Levels: No pleiotropy Pleiotropyfinal.df$arch_level_sub <-
factor(final.df$arch_level_sub,
levels=c("1-trait", "2-trait-no-pleiotropy-equal-S",
"2-trait-pleiotropy-equal-S",
"2-trait-no-pleiotropy-unequal-S",
"2-trait-pleiotropy-unequal-S"), ordered=TRUE)
levels(final.df$arch_level_sub) <- c("1 trait", "2 traits, no pleiotropy, equal S",
"2 traits, pleiotropy, equal S",
"2 traits, no pleiotropy, unequal S",
"2 traits, pleiotropy, unequal S")
levels(final.df$arch_level_sub) <- c("1 trait", "2 traits\nno pleiotropy\nequal S",
"2 traits\npleiotropy\nequal S",
"2 traits\nno pleiotropy\nunequal S",
"2 traits\npleiotropy\nunequal S")
final.df$demog_level <- factor(final.df$demog_level, levels=c("SS-Clines", "SS-Mtn", "Est-Clines"), ordered=TRUE)
final.df$demog_level_sub<- factor(final.df$demog_level_sub, levels=c("N-equal_m-constant", "N-equal_m_breaks", "N-cline-N-to-S_m-constant", "N-cline-center-to-edge_m-constant", "N-variable_m-variable"), ordered=TRUE)
final.df$demog_level_sub <- as.factor(final.df$demog_level_sub)
levels(final.df$demog_level_sub)## [1] "N-equal_m-constant" "N-equal_m_breaks"
## [3] "N-cline-N-to-S_m-constant" "N-cline-center-to-edge_m-constant"
## [5] "N-variable_m-variable"levels(final.df$demog_level_sub) <- c("N equal\nm constant", "N equal\nm breaks", "N latitude cline\nm constant", "N central cline\nm constant", "N variable\nm variable") #warning, make sure in same order as previous code
levels(final.df$demog_level_sub)## [1] "N equal\nm constant" "N equal\nm breaks"
## [3] "N latitude cline\nm constant" "N central cline\nm constant"
## [5] "N variable\nm variable"Plot mean FST
### Check for missing data
sum(is.na(final.df$meanFst))## [1] 0summary(final.df$meanFst)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.05211 0.08755 0.13028 0.13349 0.15535 0.49555sum(is.na(final.df$arch_level_sub))## [1] 0sum(is.na(final.df$demog_level))## [1] 0nrow(final.df)## [1] 2250### Table of mean FST for different levels - no evidence of missing data
tapply(final.df$meanFst, list(final.df$demog_level, final.df$demog_level_sub), mean)## N equal\nm constant N equal\nm breaks N latitude cline\nm constant
## SS-Clines 0.05787589 0.08295596 0.1248306
## SS-Mtn 0.06375824 0.08924858 0.1331580
## Est-Clines 0.08761539 0.13069710 0.1582141
## N central cline\nm constant N variable\nm variable
## SS-Clines 0.1297529 0.1607639
## SS-Mtn 0.1388368 0.1676450
## Est-Clines 0.1872237 0.2897629tapply(final.df$meanFst, list(final.df$demog_level, final.df$demog_level_sub), length)## N equal\nm constant N equal\nm breaks N latitude cline\nm constant
## SS-Clines 150 150 150
## SS-Mtn 150 150 150
## Est-Clines 150 150 150
## N central cline\nm constant N variable\nm variable
## SS-Clines 150 150
## SS-Mtn 150 150
## Est-Clines 150 150tapply(final.df$meanFst, list(final.df$demog_level, final.df$arch_level_sub), mean)## 1 trait 2 traits\nno pleiotropy\nequal S
## SS-Clines 0.1096657 0.1146675
## SS-Mtn 0.1117598 0.1237163
## Est-Clines 0.1824936 0.1767900
## 2 traits\npleiotropy\nequal S 2 traits\nno pleiotropy\nunequal S
## SS-Clines 0.1135987 0.1086960
## SS-Mtn 0.1222853 0.1185259
## Est-Clines 0.1682394 0.1629417
## 2 traits\npleiotropy\nunequal S
## SS-Clines 0.1095514
## SS-Mtn 0.1163594
## Est-Clines 0.1630485tapply(final.df$meanFst, list(final.df$demog_level, final.df$arch_level_sub), length)## 1 trait 2 traits\nno pleiotropy\nequal S
## SS-Clines 150 150
## SS-Mtn 150 150
## Est-Clines 150 150
## 2 traits\npleiotropy\nequal S 2 traits\nno pleiotropy\nunequal S
## SS-Clines 150 150
## SS-Mtn 150 150
## Est-Clines 150 150
## 2 traits\npleiotropy\nunequal S
## SS-Clines 150
## SS-Mtn 150
## Est-Clines 150### Distribution of FST as a funciton of architecture
f <- ggplot(final.df) + geom_histogram(aes(x=meanFst, fill=arch_level_sub), binwidth=0.01, color="grey") + ggtheme +
coord_cartesian(xlim = c(0.0, 0.5)) + # does not give an error for bins with missing values
#xlim(0,0.5) + #gives an error for bins with missing values
facet_grid(demog_level~.) + scale_fill_viridis(option="magma", discrete=TRUE, name="Pleiotropy level") + xlab("Fst") + ylab("Number of simulations") + theme(legend.position="bottom") + guides(fill = guide_legend(reverse = TRUE)) + ggtitle("A")
f
pdf(paste0(outputs,"FST_Demog.pdf"), width=8, height=6)
### Distribution of FST as a funciton of demography
ggplot(final.df) + geom_histogram(aes(x=meanFst, fill=demog_level_sub), binwidth=0.01, color="grey", na.rm=TRUE) + ggtheme + coord_cartesian(xlim = c(0, 0.5), ylim=c(0,160)) + facet_grid(demog_level~., scales="free") + scale_fill_viridis(option="mako", discrete=TRUE, name="Demography level") + xlab("Fst") + ylab("Number of simulations") + theme(legend.position="bottom") + geom_hline(yintercept=0, color="white", size=1) + geom_hline(yintercept=0, color="grey10", size=0.1)
dev.off()## svg
## 2 ggplot(final.df) + geom_histogram(aes(x=meanFst, fill=demog_level_sub, position="stack"), binwidth=0.01, color="grey") + ggtheme + xlim(0,0.5) + facet_grid(demog_level~.) + scale_fill_viridis(option="mako", discrete=TRUE, name="Demography level") + xlab("Fst") + ylab("Number of simulations") + theme(legend.position="bottom") ## Warning: Ignoring unknown aesthetics: position## Warning: Removed 30 rows containing missing values (geom_bar).
ggplot(final.df, aes(x=as.factor(demog_level_sub), y=meanFst, fill=demog_level)) + geom_boxplot(color="grey") + ggtheme + xlab("Demography") + ylab("overall FST") + ylim(0,0.5) + scale_fill_viridis(discrete=TRUE, name="Landscape")
a<-tapply(final.df$meanFst,final.df$demog_name, mean, na.rm=TRUE)
b<- tapply(final.df$final_LA,final.df$demog_name, mean, na.rm=TRUE)
cbind(Fst=a,LA=b)## Fst LA
## SS-Clines_N-equal_m-constant 0.05787589 0.4829661
## SS-Mtn_N-equal_m-constant 0.06375824 0.4866809
## Est-Clines_N-equal_m-constant 0.08761539 0.4280998
## SS-Clines_N-cline-N-to-S_m-constant 0.12483060 0.4772134
## SS-Mtn_N-cline-N-to-S_m-constant 0.13315802 0.4776251
## Est-Clines_N-cline-N-to-S_m-constant 0.15821409 0.4212553
## SS-Clines_N-cline-center-to-edge_m-constant 0.12975290 0.4762261
## SS-Mtn_N-cline-center-to-edge_m-constant 0.13883682 0.4791905
## Est-Clines_N-cline-center-to-edge_m-constant 0.18722368 0.4203116
## SS-Clines_N-equal_m_breaks 0.08295596 0.4877002
## SS-Mtn_N-equal_m_breaks 0.08924858 0.4903118
## Est-Clines_N-equal_m_breaks 0.13069710 0.4295439
## SS-Clines_N-variable_m-variable 0.16076391 0.4713436
## SS-Mtn_N-variable_m-variable 0.16764502 0.4669084
## Est-Clines_N-variable_m-variable 0.28976287 0.4251321ggplot(final.df, aes(x=as.factor(N_traits), y=meanFst, fill=demog_level)) + geom_boxplot(color="grey") + ggtheme + xlab("Number of traits") + ylab("meanFst") + scale_color_viridis(discrete=TRUE) + theme(legend.position = "none") + ylim(0,1) 
Degree local adaptation
Visualize the degree of local adapation
summary(final.df$final_LA)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2509 0.4033 0.4821 0.4614 0.5201 0.5796 l1 <- ggplot(final.df) + geom_histogram(aes(x=final_LA, fill=arch_level_sub), binwidth=0.01, color="grey") + ggtheme + coord_cartesian(xlim = c(0, 0.75)) + facet_grid(demog_level~.) + scale_fill_viridis(option="magma", discrete=TRUE, name="Pleiotropy level") + xlab("Amount of local adaptation") + ylab("Number of simulations") + theme(legend.position="bottom") +
guides(fill = guide_legend(reverse = TRUE)) + ggtitle("A") + geom_hline(yintercept=0, color="white", size=1) + geom_hline(yintercept=0, color="grey10", size=0.1)
l1
ggplot(final.df) + geom_histogram(aes(x=final_LA, fill=demog_level_sub), binwidth=0.01, color="grey") + ggtheme + coord_cartesian(xlim = c(0, 0.6)) + facet_grid(demog_level~.) + scale_fill_viridis(option="mako", discrete=TRUE, name="Demography level") + xlab("Amount of local adaptation") + ylab("Number of simulations") + theme(legend.position="bottom") +
guides(fill = guide_legend(reverse = FALSE)) + ggtitle("A") + geom_hline(yintercept=0, color="white", size=1) + geom_hline(yintercept=0, color="grey10", size=0.1)
l2<- ggplot(final.df) +
geom_point(aes(x=subsamp_corr_phen_temp,
y=subsamp_corr_phen_sal,
color=final_LA,shape=demog_level)) +
ggtheme +
scale_color_viridis(option="mako", name="Local\nAdaptation", begin=0.2, end=1) + coord_cartesian(xlim = c(0.25,1)) + ylim(0.25,1) + xlab("Cor(temp. phenotype, deme temperature)") + ylab("Cor(Env2 phenotype, deme Env2)") + ggtitle("B") +labs(shape="Landscape") + geom_hline(yintercept=0, color="white", size=1) + geom_hline(yintercept=0, color="grey10", size=0.1)
l2## Warning: Using shapes for an ordinal variable is not advised## Warning: Removed 450 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 1 rows containing missing values (geom_hline).
# in this case, the removed points have an NA because the "sal" trait was not adaptive in 450 simulations:
# ggplot(final.df, aes(x=as.factor(N_traits), y=meanFst, fill=demog_level)) + geom_boxplot(color="grey") + ggtheme + xlab("Number of traits") + ylab("meanFst") + scale_color_viridis(discrete=TRUE) + theme(legend.position = "none") + ylim(0,1) + ggtitle("C")
sum(is.na(final.df$subsamp_corr_phen_temp))## [1] 0 sum(is.na(final.df$subsamp_corr_phen_sal))## [1] 450pdf(paste0(outputs,"AmountLA.pdf"), width=7, height=8)
grid.arrange(l1,l2, nrow=2)## Warning: Using shapes for an ordinal variable is not advised## Warning: Removed 450 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 1 rows containing missing values (geom_hline).dev.off()## svg
## 2Number of loci
# Create a dataframe for plotting
numloc_df <- gather(final.df, key=filter, value=num_loci_causal, num_causal_prefilter, num_causal_postfilter)
numloc_df$filter <- factor(numloc_df$filter, levels = c("num_causal_prefilter", "num_causal_postfilter"), ordered=TRUE)
levels(numloc_df$filter) <- c("Pre MAF filter", "Post filter MAF > 0 .01")
str(numloc_df$filter)## Ord.factor w/ 2 levels "Pre MAF filter"<..: 1 1 1 1 1 1 1 1 1 1 ...tapply( numloc_df$num_loci_causal, list(numloc_df$filter,numloc_df$arch_level), mean, na.rm=TRUE)## oligogenic mod.\npolygenic highly\npolygenic
## Pre MAF filter 12.36267 646.8533 3042.847
## Post filter MAF > 0 .01 7.83600 58.1440 498.948str(numloc_df$arch_level)## Ord.factor w/ 3 levels "oligogenic"<"mod.\npolygenic"<..: 3 3 3 3 3 3 3 3 3 3 ...# Plot QTN loci
m<- ggplot(numloc_df ) + geom_boxplot(aes(y=num_loci_causal,x = arch_level, fill=filter), color="grey70") + ggtheme + ylim(0,4000) + ylab("Number of causal loci") + xlab("Architecture") + scale_fill_viridis(option="mako", discrete=TRUE)
m
# Check no missing data
sum(is.na(final.df$num_neut_prefilter))## [1] 0sum(is.na(final.df$num_neut_postfilter))## [1] 0# Plot neutral loci
n <- ggplot(final.df) + geom_violin(aes(y=num_neut_postfilter, x=demog_level_sub, fill=demog_level), color="grey70") + ggtheme + ylab("Number of neutral loci\nafter sampling and MAF filter") + xlab("Demography") + guides(fill=guide_legend(title="Landscape")) + scale_fill_viridis(option="viridis", begin=0, end=1, discrete=TRUE)
n
tapply(final.df$num_neut_postfilter, list(final.df$demog_level_sub), median, na.rm=TRUE)## N equal\nm constant N equal\nm breaks
## 31450.0 31997.0
## N latitude cline\nm constant N central cline\nm constant
## 27139.5 25316.0
## N variable\nm variable
## 10753.5tapply(final.df$num_neut_postfilter, list(final.df$demog_level_sub), mean, na.rm=TRUE)## N equal\nm constant N equal\nm breaks
## 31450.10 32042.55
## N latitude cline\nm constant N central cline\nm constant
## 28013.50 25384.45
## N variable\nm variable
## 10641.12pdf(paste0(outputs,"NumLociPrePostfilter.pdf"), width=7, height=8)
grid.arrange(m, n, nrow=2)
dev.off()## svg
## 2K (Number of populations)
# What determines K?
K_mod <- summary(aov(K~arch + demog_level_sub + demog_level + arch*demog_level_sub*demog_level, data=final.df))
K_mod## Df Sum Sq Mean Sq F value Pr(>F)
## arch 14 144 10 3.630 5.36e-06 ***
## demog_level_sub 4 1458 364 128.881 < 2e-16 ***
## demog_level 2 6885 3443 1217.544 < 2e-16 ***
## arch:demog_level_sub 56 262 5 1.654 0.00183 **
## arch:demog_level 28 199 7 2.513 2.10e-05 ***
## demog_level_sub:demog_level 8 1972 246 87.177 < 2e-16 ***
## arch:demog_level_sub:demog_level 112 633 6 1.998 8.25e-09 ***
## Residuals 2025 5726 3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Percent of variance (SS) explained by each
pervarK <- round(K_mod[[1]][,2]/sum(K_mod[[1]][,2]),2)
data.frame(name=rownames(K_mod[[1]]),pervarK)## name pervarK
## 1 arch 0.01
## 2 demog_level_sub 0.08
## 3 demog_level 0.40
## 4 arch:demog_level_sub 0.02
## 5 arch:demog_level 0.01
## 6 demog_level_sub:demog_level 0.11
## 7 arch:demog_level_sub:demog_level 0.04
## 8 Residuals 0.33 # mostly determined by demography
summary(final.df$K)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 4.121 5.000 11.000pdf(paste0(outputs,"K_by_demog.pdf"), width=6, height=6)
ggplot(final.df) + geom_histogram(aes(x=K, fill=demog_level), binwidth=1, color="grey") + ggtheme + coord_cartesian(xlim = c(0, 11)) + facet_grid(demog_level_sub~.) + xlab("Number of population clusters (K)") + ylab("Number of simulations") + theme(legend.position="bottom") +
guides(fill = guide_legend(reverse = FALSE)) + geom_hline(yintercept=0, color="white", size=1) + geom_hline(yintercept=0, color="grey10", size=0.1) + scale_fill_viridis(option="viridis", begin=0, end=1, discrete=TRUE, name="Landscape")
dev.off()## svg
## 2Statistical models
MODELS FOR DIVERGENCE (FST) AND FOR AMOUNT Local adaptation (LA)
levels(final.df$arch)## [1] "highly-polygenic_1-trait"
## [2] "highly-polygenic_2-trait-no-pleiotropy-equal-S"
## [3] "highly-polygenic_2-trait-no-pleiotropy-unequal-S"
## [4] "highly-polygenic_2-trait-pleiotropy-equal-S"
## [5] "highly-polygenic_2-trait-pleiotropy-unequal-S"
## [6] "mod-polygenic_1-trait"
## [7] "mod-polygenic_2-trait-no-pleiotropy-equal-S"
## [8] "mod-polygenic_2-trait-no-pleiotropy-unequal-S"
## [9] "mod-polygenic_2-trait-pleiotropy-equal-S"
## [10] "mod-polygenic_2-trait-pleiotropy-unequal-S"
## [11] "oliogenic_1-trait"
## [12] "oliogenic_2-trait-no-pleiotropy-equal-S"
## [13] "oliogenic_2-trait-no-pleiotropy-unequal-S"
## [14] "oliogenic_2-trait-pleiotropy-equal-S"
## [15] "oliogenic_2-trait-pleiotropy-unequal-S"levels(final.df$demog_level)## [1] "SS-Clines" "SS-Mtn" "Est-Clines"levels(final.df$demog_level_sub)## [1] "N equal\nm constant" "N equal\nm breaks"
## [3] "N latitude cline\nm constant" "N central cline\nm constant"
## [5] "N variable\nm variable"## ANOVA for degree LA
LA_mod <- summary(aov(final_LA~arch + demog_level_sub + demog_level + arch*demog_level_sub*demog_level, data=final.df))
LA_mod## Df Sum Sq Mean Sq F value Pr(>F)
## arch 14 12.280 0.8771 11593.832 <2e-16 ***
## demog_level_sub 4 0.065 0.0162 214.755 <2e-16 ***
## demog_level 2 1.499 0.7495 9907.658 <2e-16 ***
## arch:demog_level_sub 56 0.034 0.0006 7.943 <2e-16 ***
## arch:demog_level 28 0.862 0.0308 407.106 <2e-16 ***
## demog_level_sub:demog_level 8 0.018 0.0023 30.331 <2e-16 ***
## arch:demog_level_sub:demog_level 112 0.011 0.0001 1.283 0.0272 *
## Residuals 2025 0.153 0.0001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Perent of SS for each explanatory variable
pervarLA <- round(LA_mod[[1]][,2]/sum(LA_mod[[1]][,2]),2)
## ANOVA for FST
FST_mod <- summary(aov(meanFst ~ arch + demog_level_sub + demog_level + arch*demog_level_sub*demog_level, data=final.df))
FST_mod## Df Sum Sq Mean Sq F value Pr(>F)
## arch 14 0.035 0.0025 6.982 3.09e-14 ***
## demog_level_sub 4 4.839 1.2099 3426.501 < 2e-16 ***
## demog_level 2 1.578 0.7889 2234.390 < 2e-16 ***
## arch:demog_level_sub 56 0.032 0.0006 1.597 0.00353 **
## arch:demog_level 28 0.046 0.0016 4.629 1.82e-14 ***
## demog_level_sub:demog_level 8 0.655 0.0819 231.995 < 2e-16 ***
## arch:demog_level_sub:demog_level 112 0.047 0.0004 1.187 0.09334 .
## Residuals 2025 0.715 0.0004
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Perent of SS for each explanatory variable
pervarFST <- round(FST_mod[[1]][,2]/sum(FST_mod[[1]][,2]),2)
data.frame(name=rownames(FST_mod[[1]]),pervarLA, pervarFST)## name pervarLA pervarFST
## 1 arch 0.82 0.00
## 2 demog_level_sub 0.00 0.61
## 3 demog_level 0.10 0.20
## 4 arch:demog_level_sub 0.00 0.00
## 5 arch:demog_level 0.06 0.01
## 6 demog_level_sub:demog_level 0.00 0.08
## 7 arch:demog_level_sub:demog_level 0.00 0.01
## 8 Residuals 0.01 0.09MODELS FOR allele frequency (AF) clines
# Create a dataframe for analysis with both traits
stat_df <- gather(final.df, key=trait, value=cor_TPR, cor_TPR_temp, cor_TPR_sal)
tail(stat_df)## seed
## 4495 1233338
## 4496 1233339
## 4497 1233340
## 4498 1233341
## 4499 1233342
## 4500 1233343
## level
## 4495 oliogenic_2-trait-pleiotropy-unequal-S__SS-Clines_N-variable_m-variable
## 4496 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-cline-center-to-edge_m-constant
## 4497 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-cline-N-to-S_m-constant
## 4498 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-equal_m_breaks
## 4499 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-equal_m-constant
## 4500 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable
## reps arch
## 4495 10 oliogenic_2-trait-pleiotropy-unequal-S
## 4496 10 oliogenic_2-trait-pleiotropy-unequal-S
## 4497 10 oliogenic_2-trait-pleiotropy-unequal-S
## 4498 10 oliogenic_2-trait-pleiotropy-unequal-S
## 4499 10 oliogenic_2-trait-pleiotropy-unequal-S
## 4500 10 oliogenic_2-trait-pleiotropy-unequal-S
## demog_name demog_level_sub
## 4495 SS-Clines_N-variable_m-variable N variable\nm variable
## 4496 SS-Mtn_N-cline-center-to-edge_m-constant N central cline\nm constant
## 4497 SS-Mtn_N-cline-N-to-S_m-constant N latitude cline\nm constant
## 4498 SS-Mtn_N-equal_m_breaks N equal\nm breaks
## 4499 SS-Mtn_N-equal_m-constant N equal\nm constant
## 4500 SS-Mtn_N-variable_m-variable N variable\nm variable
## demog_level MIG_x MIG_y xcline ycline demog METAPOP_SIDE_x METAPOP_SIDE_y
## 4495 SS-Clines 0.03 0.03 linear linear SS 10 10
## 4496 SS-Mtn 0.03 0.03 V linear SS 10 10
## 4497 SS-Mtn 0.03 0.03 V linear SS 10 10
## 4498 SS-Mtn 0.03 0.03 V linear SS 10 10
## 4499 SS-Mtn 0.03 0.03 V linear SS 10 10
## 4500 SS-Mtn 0.03 0.03 V linear SS 10 10
## Nequal isVariableM MIG_breaks arch_level_sub arch_level
## 4495 3 1 0 2 traits\npleiotropy\nunequal S oligogenic
## 4496 4 0 0 2 traits\npleiotropy\nunequal S oligogenic
## 4497 2 0 0 2 traits\npleiotropy\nunequal S oligogenic
## 4498 0 0 1 2 traits\npleiotropy\nunequal S oligogenic
## 4499 0 0 0 2 traits\npleiotropy\nunequal S oligogenic
## 4500 3 1 0 2 traits\npleiotropy\nunequal S oligogenic
## MU_base MU_QTL_proportion SIGMA_QTN_1 SIGMA_QTN_2 SIGMA_K_1 SIGMA_K_2
## 4495 1e-07 0.001 0.4 0.4 0.5 4
## 4496 1e-07 0.001 0.4 0.4 0.5 4
## 4497 1e-07 0.001 0.4 0.4 0.5 4
## 4498 1e-07 0.001 0.4 0.4 0.5 4
## 4499 1e-07 0.001 0.4 0.4 0.5 4
## 4500 1e-07 0.001 0.4 0.4 0.5 4
## N_traits ispleiotropy n_samp_tot n_samp_per_pop sd_fitness_among_inds
## 4495 2 Pleiotropy 1000 10 0.06989773
## 4496 2 Pleiotropy 1000 10 0.10457515
## 4497 2 Pleiotropy 1000 10 0.10815036
## 4498 2 Pleiotropy 1000 10 0.09447908
## 4499 2 Pleiotropy 1000 10 0.10039771
## 4500 2 Pleiotropy 1000 10 0.11933530
## sd_fitness_among_pops final_LA K Bonf_alpha numCausalLowMAFsample
## 4495 0.03432623 0.390202 2 4.302186e-06 0
## 4496 0.05800045 0.378143 3 2.037324e-06 0
## 4497 0.05691502 0.386475 3 1.950687e-06 0
## 4498 0.04351184 0.406991 1 1.582930e-06 0
## 4499 0.04816428 0.411540 2 1.608648e-06 0
## 4500 0.06758560 0.361984 1 6.179706e-06 0
## all_corr_phen_temp subsamp_corr_phen_temp all_corr_phen_sal
## 4495 0.8105442 0.6442488 0.9331842
## 4496 0.6134233 0.5623938 0.8856847
## 4497 0.6300670 0.5378167 0.9092372
## 4498 0.7915280 0.6228879 0.9145244
## 4499 0.8020544 0.6425425 0.9127410
## 4500 0.8163458 0.6347974 0.8849650
## subsamp_corr_phen_sal num_causal_prefilter num_causal_postfilter
## 4495 0.8112850 8 5
## 4496 0.8061503 11 6
## 4497 0.8075816 8 6
## 4498 0.8166540 10 7
## 4499 0.8128461 14 9
## 4500 0.7608153 12 8
## num_non_causal num_neut_prefilter num_neut_postfilter
## 4495 11617 11843 11843
## 4496 24536 25257 25257
## 4497 25626 26975 26975
## 4498 31580 31942 31942
## 4499 31073 31405 31405
## 4500 8083 8197 8197
## num_neut_neutralgenome num_causal_temp num_causal_sal num_multiallelic
## 4495 5810 5 5 0
## 4496 12346 6 6 0
## 4497 12834 6 6 0
## 4498 15946 7 7 0
## 4499 15689 9 9 0
## 4500 3947 8 8 0
## meanFst va_temp_total va_sal_total Va_temp_sample Va_sal_sample nSNPs
## 4495 0.14301783 0.14320895 0.06757681 0.13713509 0.06623635 11622
## 4496 0.13350458 0.05299476 0.07273692 0.08494197 0.09831001 24542
## 4497 0.13740776 0.09929097 0.10670097 0.22910565 0.10650756 25632
## 4498 0.08708807 0.13554966 0.10110684 0.13249490 0.10066003 31587
## 4499 0.06704804 0.16714222 0.16315859 0.16734136 0.16310813 31082
## 4500 0.17379409 0.08556485 0.10592398 0.08049695 0.09935857 8091
## median_causal_temp_cor median_causal_sal_cor median_neut_temp_cor
## 4495 0.2754918 0.5180941 0.2414407
## 4496 0.3026247 0.4175574 0.1850624
## 4497 0.2000535 0.3990216 0.2321665
## 4498 0.2967283 0.4868710 0.2257370
## 4499 0.3428889 0.3406402 0.1621695
## 4500 0.3033895 0.3424647 0.2488890
## median_neut_sal_cor cor_VA_temp_prop cor_VA_sal_prop cor_FDR_allSNPs_temp
## 4495 0.22546761 0.8516887 0.9978557 0.9993947
## 4496 0.12090175 0.4727327 0.8630538 0.9997394
## 4497 0.10810696 0.7985926 0.7317329 0.9998285
## 4498 0.09267506 0.6560965 0.8820640 0.9995787
## 4499 0.10404740 0.1136390 0.4316080 0.9989027
## 4500 0.13587417 0.3901235 0.8777962 0.9987903
## cor_FDR_neutSNPs_temp cor_FDR_allSNPs_sal cor_FDR_neutSNPs_sal
## 4495 0.9987358 0.9985337 0.9968774
## 4496 0.9995287 0.9949749 0.9840000
## 4497 0.9996431 0.9933775 0.9732143
## 4498 0.9991545 0.9692308 0.8378378
## 4499 0.9976285 0.9916201 0.9700000
## 4500 0.9974958 0.9913043 0.9743590
## num_causal_sig_temp_corr num_causal_sig_sal_corr
## 4495 2 4
## 4496 1 4
## 4497 1 3
## 4498 3 6
## 4499 3 3
## 4500 3 4
## num_notCausal_sig_temp_corr num_notCausal_sig_sal_corr
## 4495 3302 2724
## 4496 3836 792
## 4497 5830 450
## 4498 7118 189
## 4499 2731 355
## 4500 2477 456
## num_neut_sig_temp_corr num_neut_sig_sal_corr cor_AUCPR_temp_allSNPs
## 4495 1580 1277 0.0056220144
## 4496 2121 246 0.0007501130
## 4497 2801 109 0.0003226524
## 4498 3545 31 0.0003352525
## 4499 1262 97 0.0050843070
## 4500 1195 152 0.0013870159
## cor_AUCPR_temp_neutSNPs cor_AUCPR_sal_allSNPs cor_AUCPR_sal_neutSNPs
## 4495 0.0161350441 0.09787865 0.4036255
## 4496 0.0013167008 0.24012890 0.3423103
## 4497 0.0006630059 0.17086955 0.1834894
## 4498 0.0006712386 0.40553013 0.7821320
## 4499 0.0109923069 0.11638113 0.1367044
## 4500 0.0029380064 0.02439708 0.2860155
## cor_af_temp_noutliers cor_af_sal_noutliers cor_FPR_temp_neutSNPs
## 4495 3304 2728 0.27194492
## 4496 3837 796 0.17179653
## 4497 5831 453 0.21824840
## 4498 7121 195 0.22231281
## 4499 2734 358 0.08043852
## 4500 2480 460 0.30276159
## cor_FPR_sal_neutSNPs LEA3.2_lfmm2_Va_temp_prop LEA3.2_lfmm2_Va_sal_prop
## 4495 0.219793460 0.0000000 0.9578289
## 4496 0.019925482 0.4727327 0.9998981
## 4497 0.008493065 0.0000000 0.9412170
## 4498 0.001944061 0.0000000 0.8820640
## 4499 0.006182676 0.0000000 0.9171033
## 4500 0.038510261 0.0000000 0.8777962
## LEA3.2_lfmm2_TPR_temp LEA3.2_lfmm2_TPR_sal LEA3.2_lfmm2_FDR_allSNPs_temp
## 4495 0.0000000 0.6000000 NA
## 4496 0.1666667 0.8333333 0
## 4497 0.0000000 0.6666667 NA
## 4498 0.0000000 0.8571429 NA
## 4499 0.0000000 0.5555556 NA
## 4500 0.0000000 0.5000000 NA
## LEA3.2_lfmm2_FDR_allSNPs_sal LEA3.2_lfmm2_FDR_neutSNPs_temp
## 4495 0.9302326 NA
## 4496 0.9180328 0
## 4497 0.9680000 NA
## 4498 0.8888889 NA
## 4499 0.9253731 NA
## 4500 0.9090909 NA
## LEA3.2_lfmm2_FDR_neutSNPs_sal LEA3.2_lfmm2_AUCPR_temp_allSNPs
## 4495 0.0 0.2881630
## 4496 0.0 0.5981415
## 4497 0.2 0.2337020
## 4498 0.0 0.5925314
## 4499 0.0 0.2628556
## 4500 0.0 0.2117759
## LEA3.2_lfmm2_AUCPR_temp_neutSNPs LEA3.2_lfmm2_AUCPR_sal_allSNPs
## 4495 0.6313777 0.2881630
## 4496 0.8340475 0.5981415
## 4497 0.7153403 0.2337020
## 4498 1.0000000 0.5925314
## 4499 0.5658529 0.2628556
## 4500 0.5088647 0.2117759
## LEA3.2_lfmm2_AUCPR_sal_neutSNPs LEA3.2_lfmm2_mlog10P_tempenv_noutliers
## 4495 0.6313777 0
## 4496 0.8340475 1
## 4497 0.7153403 0
## 4498 1.0000000 0
## 4499 0.5658529 0
## 4500 0.5088647 0
## LEA3.2_lfmm2_mlog10P_salenv_noutliers LEA3.2_lfmm2_num_causal_sig_temp
## 4495 43 0
## 4496 61 1
## 4497 125 0
## 4498 54 0
## 4499 67 0
## 4500 44 0
## LEA3.2_lfmm2_num_neut_sig_temp LEA3.2_lfmm2_num_causal_sig_sal
## 4495 0 3
## 4496 0 5
## 4497 0 4
## 4498 0 6
## 4499 0 5
## 4500 0 4
## LEA3.2_lfmm2_num_neut_sig_sal LEA3.2_lfmm2_FPR_neutSNPs_temp
## 4495 0 0
## 4496 0 0
## 4497 1 0
## 4498 0 0
## 4499 0 0
## 4500 0 0
## LEA3.2_lfmm2_FPR_neutSNPs_sal RDA1_propvar RDA2_propvar RDA1_propvar_corr
## 4495 0.000000e+00 0.550 0.450 0.665
## 4496 0.000000e+00 0.742 0.258 0.558
## 4497 7.791803e-05 0.716 0.284 0.772
## 4498 0.000000e+00 0.835 0.165 0.752
## 4499 0.000000e+00 0.684 0.316 0.624
## 4500 0.000000e+00 0.739 0.261 0.673
## RDA2_propvar_corr RDA1_temp_cor RDA1_sal_cor RDA2_temp_cor RDA2_sal_cor
## 4495 0.335 0.5015364 -0.865136554 -0.865136554 -0.5015364
## 4496 0.442 0.9999502 -0.009981000 0.009981000 0.9999502
## 4497 0.228 0.9999683 -0.007965626 0.007965626 0.9999683
## 4498 0.248 0.9991421 0.041413317 -0.041413317 0.9991421
## 4499 0.376 0.9996593 0.026102226 -0.026102226 0.9996593
## 4500 0.327 0.9800515 0.198743495 -0.198743495 0.9800515
## RDA_Va_temp_prop RDA_Va_temp_prop_corr RDA_Va_sal_prop
## 4495 0.4763587 0.8519800 0.9578289
## 4496 0.9996855 0.9996855 0.9998981
## 4497 0.9995510 0.2009584 0.9742365
## 4498 0.9677581 0.9677581 0.8820640
## 4499 0.9861608 0.9861608 0.9986455
## 4500 0.8073374 0.8955353 0.8777962
## RDA_Va_sal_prop_corr RDA_TPR RDA_TPR_corr RDA_FDR_allSNPs
## 4495 0.7328738 0.6000000 0.6000000 0.9931193
## 4496 0.9998981 0.8333333 0.8333333 0.9929478
## 4497 0.9412170 0.8333333 0.6666667 0.9968133
## 4498 0.8820640 0.8571429 0.8571429 0.9963280
## 4499 0.9986455 0.8888889 0.8888889 0.9944328
## 4500 0.9096928 0.5000000 0.6250000 0.9838710
## RDA_FDR_allSNPs_corr num_RDA_sig_causal num_RDA_sig_neutral
## 4495 0.9934211 3 118
## 4496 0.9932249 5 230
## 4497 0.9972318 5 573
## 4498 0.9955720 6 597
## 4499 0.9951190 8 479
## 4500 0.9814815 4 62
## num_RDA_sig_causal_corr num_RDA_sig_neutral_corr RDA_FDR_neutSNPs
## 4495 3 152 0.9752066
## 4496 5 244 0.9787234
## 4497 4 523 0.9913495
## 4498 6 486 0.9900498
## 4499 8 498 0.9835729
## 4500 5 92 0.9393939
## RDA_FDR_neutSNPs_corr RDA_AUCPR_allSNPs RDA_AUCPR_neutSNPs
## 4495 0.9806452 0.1317274 0.6023902
## 4496 0.9799197 0.3896916 0.6725673
## 4497 0.9924099 0.1915153 0.5139129
## 4498 0.9878049 0.3830535 0.8575997
## 4499 0.9841897 0.1615562 0.6298882
## 4500 0.9484536 0.2032180 0.5029961
## RDA_AUCPR_neutSNPs_corr RDA_FPR_neutSNPs RDA_FPR_neutSNPs_corr
## 4495 0.2991643 0.02030981 0.02616179
## 4496 0.6819382 0.01862952 0.01976349
## 4497 0.5028327 0.04464703 0.04075113
## 4498 0.8575851 0.03743886 0.03047786
## 4499 0.6418724 0.03053095 0.03174198
## 4500 0.3320560 0.01570813 0.02330884
## RDA_RDAmutpred_cor_tempEffect RDA_RDAmutpred_cor_salEffect
## 4495 0.6000000 0.6000000
## 4496 0.8666667 0.6000000
## 4497 0.6000000 0.6000000
## 4498 0.5238095 0.3333333
## 4499 0.5555556 0.7777778
## 4500 0.5000000 0.2857143
## RDA_absRDAmutpred_cor_tempVa RDA_absRDAmutpred_cor_salVa
## 4495 0.020465239 0.01871735
## 4496 0.011409116 0.01826038
## 4497 0.002784945 0.01793393
## 4498 0.017384447 0.02012018
## 4499 0.017285637 0.01862984
## 4500 0.020696744 0.02400657
## RDA_RDAmutpred_cor_tempEffect_structcorr
## 4495 0.36606915
## 4496 -0.07001212
## 4497 -0.07228499
## 4498 0.15434486
## 4499 0.12235804
## 4500 0.27521121
## RDA_RDAmutpred_cor_salEffect_structcorr
## 4495 0.9208180
## 4496 0.8150453
## 4497 0.8026655
## 4498 0.7288245
## 4499 0.7696911
## 4500 0.6602084
## RDA_absRDAmutpred_cor_tempVa_structcorr
## 4495 0.04635961
## 4496 0.01113341
## 4497 0.02638952
## 4498 0.06395474
## 4499 0.07991070
## 4500 0.08556552
## RDA_absRDAmutpred_cor_salVa_structcorr
## 4495 0.07122715
## 4496 0.10423641
## 4497 0.09238268
## 4498 0.11222190
## 4499 0.08011436
## 4500 0.11534351
## RDA_cor_RDA20000temppredict_tempPhen RDA_cor_RDA20000salpredict_salPhen
## 4495 0.6025624 0.6970451
## 4496 0.5688492 0.7562831
## 4497 0.5073146 0.6932780
## 4498 0.5917726 0.7525031
## 4499 0.6272143 0.7680113
## 4500 0.5661217 0.6653819
## RDA_cor_RDA20000temppredict_tempPhen_structcorr
## 4495 0.02969630
## 4496 0.08385813
## 4497 0.18613306
## 4498 0.05323003
## 4499 0.12648538
## 4500 0.23854195
## RDA_cor_RDA20000salpredict_salPhen_structcorr cor_PC1_temp cor_PC1_sal
## 4495 0.2459241 -0.49592948 0.707550286
## 4496 0.7608327 -0.03745187 0.010848786
## 4497 0.6778338 -0.42331353 -0.036871726
## 4498 0.7661762 -0.96800660 -0.035807738
## 4499 0.7672155 -0.04223546 0.005524909
## 4500 0.6262283 0.78322231 0.099152034
## cor_PC2_temp cor_PC2_sal cor_LFMMU1_temp cor_LFMMU1_sal cor_LFMMU2_temp
## 4495 -0.79041718 -0.49531715 -0.11538104 -0.196778909 0.41792225
## 4496 -0.93219015 0.01444962 -0.02798592 0.012589562 -0.15180910
## 4497 0.85072814 -0.05730822 -0.07053397 -0.038642435 -0.08318736
## 4498 -0.01224008 0.01880353 -0.01140580 -0.033516495 NA
## 4499 0.95953676 0.03045830 -0.01491703 0.005465646 0.09588969
## 4500 -0.41323121 -0.29453651 0.36840281 -0.066577901 NA
## cor_LFMMU2_sal cor_PC1_LFMMU1_temp cor_PC1_LFMMU1_sal cor_PC2_LFMMU1_temp
## 4495 -0.59810083 0.90723798 -0.7652270 -0.41739868
## 4496 -0.01327245 0.99957697 0.9980697 -0.01001944
## 4497 -0.04185963 0.92380303 0.9857150 0.37884682
## 4498 NA 0.02513634 0.9995552 -0.99878157
## 4499 -0.01555213 0.99870326 -0.9987481 0.02746745
## 4500 NA 0.80908546 -0.9986383 0.58076635
## cor_PC2_LFMMU1_sal gwas_TPR_sal gwas_TPR_temp gwas_FDR_sal_neutbase
## 4495 -6.375626e-01 1.0000000 0.8000000 0.9982047
## 4496 5.599139e-02 1.0000000 0.6666667 0.9990089
## 4497 1.607696e-01 1.0000000 0.3333333 0.9990226
## 4498 -6.278494e-05 1.0000000 0.7142857 0.9987023
## 4499 2.751841e-02 0.8888889 1.0000000 0.9985967
## 4500 -2.590842e-02 0.7500000 0.8750000 0.9975440
## gwas_FDR_temp_neutbase clinalparadigm_sal_proptop5GWASclines
## 4495 0.9944828 0.3144330
## 4496 0.9959225 0.4234528
## 4497 0.9990610 0.3182527
## 4498 0.9943439 0.1227848
## 4499 0.9601770 0.2232947
## 4500 0.9964340 0.5333333
## clinalparadigm_temp_proptop5GWASclines
## 4495 0.2817869
## 4496 0.2060261
## 4497 0.1224649
## 4498 0.2430380
## 4499 0.1273312
## 4500 0.2074074
## clinalparadigm_sal_propsigGWASclines clinalparadigm_temp_propsigGWASclines
## 4495 0.23302128 0.2378011
## 4496 0.06177726 0.1958369
## 4497 0.03441465 0.1351293
## 4498 0.01704694 0.2437673
## 4499 0.02904430 0.1463104
## 4500 0.09349593 0.2850657
## trait cor_TPR
## 4495 cor_TPR_sal 0.8000000
## 4496 cor_TPR_sal 0.6666667
## 4497 cor_TPR_sal 0.5000000
## 4498 cor_TPR_sal 0.8571429
## 4499 cor_TPR_sal 0.3333333
## 4500 cor_TPR_sal 0.5000000# Check the levels in the new dataframe
unique(stat_df$demog_level)## [1] Est-Clines SS-Clines SS-Mtn
## Levels: SS-Clines < SS-Mtn < Est-Clinesunique(stat_df$demog_level_sub)## [1] N central cline\nm constant N latitude cline\nm constant
## [3] N equal\nm breaks N equal\nm constant
## [5] N variable\nm variable
## 5 Levels: N equal\nm constant < ... < N variable\nm variableunique(stat_df$demog_name)## [1] Est-Clines_N-cline-center-to-edge_m-constant
## [2] Est-Clines_N-cline-N-to-S_m-constant
## [3] Est-Clines_N-equal_m_breaks
## [4] Est-Clines_N-equal_m-constant
## [5] Est-Clines_N-variable_m-variable
## [6] SS-Clines_N-cline-center-to-edge_m-constant
## [7] SS-Clines_N-cline-N-to-S_m-constant
## [8] SS-Clines_N-equal_m_breaks
## [9] SS-Clines_N-equal_m-constant
## [10] SS-Clines_N-variable_m-variable
## [11] SS-Mtn_N-cline-center-to-edge_m-constant
## [12] SS-Mtn_N-cline-N-to-S_m-constant
## [13] SS-Mtn_N-equal_m_breaks
## [14] SS-Mtn_N-equal_m-constant
## [15] SS-Mtn_N-variable_m-variable
## 15 Levels: SS-Clines_N-equal_m-constant ... Est-Clines_N-variable_m-variableunique(stat_df$arch_level_sub)## [1] 1 trait 2 traits\nno pleiotropy\nequal S
## [3] 2 traits\nno pleiotropy\nunequal S 2 traits\npleiotropy\nequal S
## [5] 2 traits\npleiotropy\nunequal S
## 5 Levels: 1 trait < ... < 2 traits\npleiotropy\nunequal Sunique(stat_df$arch)## [1] highly-polygenic_1-trait
## [2] highly-polygenic_2-trait-no-pleiotropy-equal-S
## [3] highly-polygenic_2-trait-no-pleiotropy-unequal-S
## [4] highly-polygenic_2-trait-pleiotropy-equal-S
## [5] highly-polygenic_2-trait-pleiotropy-unequal-S
## [6] mod-polygenic_1-trait
## [7] mod-polygenic_2-trait-no-pleiotropy-equal-S
## [8] mod-polygenic_2-trait-no-pleiotropy-unequal-S
## [9] mod-polygenic_2-trait-pleiotropy-equal-S
## [10] mod-polygenic_2-trait-pleiotropy-unequal-S
## [11] oliogenic_1-trait
## [12] oliogenic_2-trait-no-pleiotropy-equal-S
## [13] oliogenic_2-trait-no-pleiotropy-unequal-S
## [14] oliogenic_2-trait-pleiotropy-equal-S
## [15] oliogenic_2-trait-pleiotropy-unequal-S
## 15 Levels: highly-polygenic_1-trait ...## Statistical model with both traits
stat_df$trait <- factor(stat_df$trait)
levels(stat_df$trait)## [1] "cor_TPR_sal" "cor_TPR_temp" levels(stat_df$N_traits)## NULL modelsum <- summary(aov(cor_TPR ~ trait + arch + demog + demog_level + final_LA + arch*trait*demog, data=stat_df))
modelsum## Df Sum Sq Mean Sq F value Pr(>F)
## trait 1 64.20 64.20 5513.30 <2e-16 ***
## arch 14 159.27 11.38 977.03 <2e-16 ***
## demog 1 4.39 4.39 377.02 <2e-16 ***
## demog_level 1 10.19 10.19 874.72 <2e-16 ***
## final_LA 1 1.06 1.06 91.11 <2e-16 ***
## trait:arch 11 9.11 0.83 71.15 <2e-16 ***
## arch:demog 14 15.03 1.07 92.22 <2e-16 ***
## trait:demog 1 38.34 38.34 3292.79 <2e-16 ***
## trait:arch:demog 11 1.76 0.16 13.77 <2e-16 ***
## Residuals 3994 46.50 0.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 450 observations deleted due to missingness ### Percent of variation (SS) explained by each explanatory variable
pervar <- data.frame(variable = rownames(modelsum[[1]]), SS = modelsum[[1]][,2])
pervar$pervar <- round(pervar$SS/sum(pervar$SS)*100, 1)
pervar## variable SS pervar
## 1 trait 64.195195 18.3
## 2 arch 159.267513 45.5
## 3 demog 4.389962 1.3
## 4 demog_level 10.185003 2.9
## 5 final_LA 1.060798 0.3
## 6 trait:arch 9.112923 2.6
## 7 arch:demog 15.033251 4.3
## 8 trait:demog 38.340187 11.0
## 9 trait:arch:demog 1.763571 0.5
## 10 Residuals 46.504897 13.3## Statistical model with only temperature trait
modelsum <- summary(aov(cor_TPR_temp ~ arch + demog + demog_level + final_LA + arch*demog*demog_level, data=final.df))
pervar_temp <- data.frame(variable = rownames(modelsum[[1]]), SS = modelsum[[1]][,2])
pervar_temp$pervar <- round(pervar_temp$SS/sum(pervar_temp$SS)*100, 1)
pervar_temp## variable SS pervar
## 1 arch 111.4892548 76.8
## 2 demog 7.5012931 5.2
## 3 demog_level 0.5870414 0.4
## 4 final_LA 0.1096110 0.1
## 5 arch:demog 4.6914989 3.2
## 6 arch:demog_level 0.4397451 0.3
## 7 Residuals 20.3182657 14.0 ## Statistical model with only salinity trait
modelsum <- summary(aov(cor_TPR_sal ~ arch + demog + demog_level + final_LA + arch*demog*demog_level, data=final.df))
pervar_sal <- data.frame(variable = rownames(modelsum[[1]]), SS = modelsum[[1]][,2])
pervar_sal$pervar <- round(pervar_sal$SS/sum(pervar_sal$SS)*100, 1)
pervar_sal## variable SS pervar
## 1 arch 56.8911818 40.5
## 2 demog 38.5015498 27.4
## 3 demog_level 15.4485976 11.0
## 4 final_LA 0.2370578 0.2
## 5 arch:demog 9.6827706 6.9
## 6 arch:demog_level 1.0917684 0.8
## 7 Residuals 18.6684705 13.3data.frame(variable = pervar_temp$variable, temp_AFcors = pervar_temp$pervar, sal_AFcors = pervar_sal$pervar)## variable temp_AFcors sal_AFcors
## 1 arch 76.8 40.5
## 2 demog 5.2 27.4
## 3 demog_level 0.4 11.0
## 4 final_LA 0.1 0.2
## 5 arch:demog 3.2 6.9
## 6 arch:demog_level 0.3 0.8
## 7 Residuals 14.0 13.3How does demography and genetic architecture contribute to the evolution of phenotypic clines without clines in causal allele frequencies?
Number of loci with correlations - demog
# Plote temperatue clines as a function of number of traits and landscape
a<- ggplot(final.df, aes(x=as.factor(N_traits), y=cor_TPR_temp, fill=demog_level)) + geom_boxplot(color="grey") + ggtheme + xlab("Number of traits") + ylab("Proportion of causal temp. \n loci with significant clines") + scale_fill_viridis(discrete=TRUE, option="viridis", begin=0, end=1) + theme(legend.position = "none") + ylim(0,1) + ggtitle("C")
a
# Plot Env2 clines as a function of landscape (only 2 trait simulations)
forb <- final.df[final.df$N_traits==2,]
forb$N_traits <- factor(forb$N_traits)
b<- ggplot(forb, aes(x=N_traits, y=cor_TPR_sal, fill=demog_level)) + geom_boxplot(color="grey") + ggtheme + xlab("Number of traits") + ylab("Proportion of causal Env2 \n loci with significant clines") + scale_fill_viridis(discrete=TRUE, option="viridis", begin=0, end=1) + ylim(0,1) + guides(fill=guide_legend(title="Landscape")) + ggtitle("D")
b
### Pleiotropy vs no pleiotropy - only relevant for sims with 2 traits
subdf_arch <- final.df[final.df$N_traits==2,]
unique(final.df$SIGMA_K_1)## [1] 0.5 unique(subdf_arch$SIGMA_K_2)## [1] 0.5 4.0 subdf_arch$SIGMA_K_2 <- as.factor(subdf_arch$SIGMA_K_2)
my_col_arch <- c(viridis(10)[3], viridis(10)[7])
# temp only
f1 <- ggplot(subdf_arch, aes(x=arch_level, y=cor_TPR_temp, fill=as.factor(ispleiotropy))) + geom_boxplot(color="grey") + ggtheme + xlab("") + ylab("Proportion of causal temp. \n loci with significant clines") + scale_fill_manual( values=my_col_arch, labels=c("No pleiotropy", "Pleiotropy"), name="2-trait architectures") + ylim(0,1) #+ theme(legend.title=element_text("2-trait\narchitectures"))
f1
# env2 only
f2 <- ggplot(subdf_arch, aes(x=arch_level, y=cor_TPR_sal, fill=as.factor(ispleiotropy))) + geom_boxplot(color="grey") + ggtheme + xlab("") + ylab("Proportion of causal sal. \n loci with significant clines") + scale_fill_manual( values=my_col_arch, labels=c("No pleiotropy", "Pleiotropy"), name="2-trait architectures") + ylim(0,1) #+ theme(legend.title=element_text("2-trait\narchitectures"))
f2
# Combine data together from f1 and f2 for a single plot
# because they both show the same pattern
pleiotropy.df <- gather(subdf_arch, key=trait, value=cor_TPR, cor_TPR_temp, cor_TPR_sal)
fboth <- ggplot(pleiotropy.df, aes(x=arch_level, y=cor_TPR, fill=as.factor(ispleiotropy))) + geom_boxplot(color="grey") + ggtheme + xlab("") + ylab("Proportion of causal \n loci with significant clines") + scale_fill_manual( values=my_col_arch, labels=c("No pleiotropy", "Pleiotropy"), name="2-trait architectures") + ylim(0,1) + ggtitle("B")
fboth
## Phenotypic clines vs. af clines
### For each trait, I want to plot the (correlation between the trait and environment) vs. (correlation between af and environment)
final.df$N_traits2 <- final.df$N_traits
final.df$N_traits2[final.df$N_traits==1] <- "1 Trait"
final.df$N_traits2[final.df$N_traits==2] <- "2 Traits"
final.df$ispleiotropy <- factor(final.df$ispleiotropy)
head(final.df$ispleiotropy)## [1] No pleiotropy No pleiotropy No pleiotropy No pleiotropy No pleiotropy
## [6] No pleiotropy
## Levels: No pleiotropy Pleiotropyf3 <- ggplot(final.df) +
geom_point(aes(x=subsamp_corr_phen_temp, y=cor_TPR_temp, color=arch_level#,shape=ispleiotropy
), alpha=0.6, fill="grey", shape=0) +
geom_point(aes(x=subsamp_corr_phen_sal, y=cor_TPR_sal, color=arch_level#, shape=ispleiotropy
), alpha=0.6, fill="grey", shape=0) +
ggtheme + ylim(0,1) + xlim(0,1) +
xlab("Correlation(phenotype, deme environment)") +
ylab("Proportion of causal \n loci with significant clines") + scale_color_viridis(name="Genic level", discrete=TRUE, option="rocket", begin=0, end=0.9) + #scale_shape_manual(values=c(0,19),name="Pleiotropy") +
ggtitle("A") #+ #+ facet_grid(~N_traits2)
# facet_grid(~arch_level_sub)
f3## Warning: Removed 450 rows containing missing values (geom_point).
# 450 missing values expected (simulations without the Env2 trait)
pdf(paste0(outputs,"WhatDrivesNonClines.pdf"), width=6, height=8)
grid.arrange(f3, fboth,a,b, nrow=3, ncol=2, layout_matrix=matrix(c(1,1,2,2,3,4), nrow=3, byrow=TRUE))## Warning: Removed 450 rows containing missing values (geom_point).# 450 missing values expected (simulations without the Env2 trait)
dev.off()## svg
## 2Percent of GWAS hits with clines
head(final.df$clinalparadigm_temp_propsigGWASclines)## [1] 0.6050302 0.5794083 0.5593769 0.4777563 0.6314615 0.3099063sum(is.na(final.df$clinalparadigm_temp_propsigGWASclines))## [1] 1plotdf <- data.frame(propsigGWASclines=c(final.df$clinalparadigm_sal_propsigGWASclines,final.df$clinalparadigm_temp_propsigGWASclines),
propClines=c(final.df$cor_TPR_sal,final.df$cor_TPR_temp),
arch_level=factor(rep("a",nrow(final.df)*2)))
plotdf$arch_level <- c(as.character(final.df$arch_level, final.df$arch_level))
str(plotdf)## 'data.frame': 4500 obs. of 3 variables:
## $ propsigGWASclines: num NA NA NA NA NA NA NA NA NA NA ...
## $ propClines : num NA NA NA NA NA NA NA NA NA NA ...
## $ arch_level : chr "highly\npolygenic" "highly\npolygenic" "highly\npolygenic" "highly\npolygenic" ...summary(lm(propsigGWASclines~propClines*arch_level, data=plotdf))##
## Call:
## lm(formula = propsigGWASclines ~ propClines * arch_level, data = plotdf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.37677 -0.07902 -0.01619 0.05425 0.76866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001683 0.006292 0.268 0.7891
## propClines 1.051200 0.021509 48.872 < 2e-16 ***
## arch_levelmod.\npolygenic -0.020949 0.009667 -2.167 0.0303 *
## arch_leveloligogenic 0.050248 0.010200 4.927 8.71e-07 ***
## propClines:arch_levelmod.\npolygenic -0.291569 0.027169 -10.732 < 2e-16 ***
## propClines:arch_leveloligogenic -0.747232 0.024263 -30.797 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1321 on 4043 degrees of freedom
## (451 observations deleted due to missingness)
## Multiple R-squared: 0.5648, Adjusted R-squared: 0.5642
## F-statistic: 1049 on 5 and 4043 DF, p-value: < 2.2e-16pdf(paste0(outputs,"PropGWASClinal.pdf"), width=6, height=5)
ggplot(plotdf, aes(y=propsigGWASclines,
x=propClines, color=arch_level)) +
geom_point( alpha=0.5) +xlim(0,1) + ylim(0,1) + ggtheme +
geom_smooth(method="glm", level=0.95) + xlab("Proportion of QTNs that are clinal (ground truth)") + ylab("Proportion of GWAS hits that are clinal") + scale_color_viridis(name="Genic level", discrete=TRUE, option="rocket", begin=0, end=0.9) +
geom_abline(intercept=0, slope=1, color="grey")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 451 rows containing non-finite values (stat_smooth).## Warning: Removed 451 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_smooth).dev.off()## svg
## 2Percent of Va explained by clinal loci
Same plots as above, but for the percent of the additive genetic variance (Va). If clinal loci explain a major proportion of Va, then it could be argued that QTN loci that do not have clinal patterns are not as “important” to discover.
# Reorganize data for plotting
pleiotropy.df2 <- gather(subdf_arch, key=trait, value=cor_VA_prop, cor_VA_temp_prop, cor_VA_sal_prop)
pleiotropy.df3 <- gather(subdf_arch, key=trait, value=cor_TPR, cor_TPR_temp, cor_TPR_sal)
fboth2 <- ggplot() +
geom_boxplot(aes(x=arch_level, y=1-cor_TPR), data=pleiotropy.df3, color="grey70", alpha=0.8, fill="grey90") +
geom_boxplot(aes(x=arch_level, y=1-cor_VA_prop, fill=arch_level), data=pleiotropy.df2, color="grey20") + ggtheme + xlab("") + ylab("Proportion of VA \n explained by non-clinal QTNs") + scale_fill_viridis(option="rocket", discrete=TRUE, end=1) + ylim(0,1) + ggtitle("A") + theme(legend.position = "none")
fboth2
a1<- ggplot() +
geom_boxplot(aes(x=demog_level, y=1-cor_TPR_temp), data=final.df, color="grey70", alpha=0.8, fill="grey90") +
geom_boxplot(data=final.df, aes(x=demog_level, y=1-cor_VA_temp_prop, fill=demog_level),color="grey20") + ggtheme + xlab("Landscape") + ylab("Proportion of VA in temp. \n explained by non-clinal QTNs") + scale_fill_viridis(discrete=TRUE, option="viridis", begin=0, end=1) + theme(legend.position = "none")+ ylim(0,1) + ggtitle("A")
a1
b1<- ggplot()+
geom_boxplot(aes(x=demog_level, y=1-cor_TPR_sal), data=final.df, color="grey70", alpha=0.8, fill="grey90") +
geom_boxplot(data=final.df, aes(x=demog_level, y=1-cor_VA_sal_prop, fill=demog_level),color="grey20") + ggtheme + xlab("Landscape") + ylab("Proportion of VA in Env2 \n explained by non-clinal QTNs") + scale_fill_viridis(discrete=TRUE, option="viridis", begin=0, end=1) + theme(legend.position = "none")+ ylim(0,1) + ggtitle("B")
b1## Warning: Removed 450 rows containing non-finite values (stat_boxplot).
pdf(paste0(outputs,"WhatDrivesNonClines_VA.pdf"), width=7, height=3)
# grid.arrange(fboth2, a1,b1, nrow=2, ncol=2, layout_matrix=matrix(c(1,1,2,3), nrow=2, byrow=TRUE))
grid.arrange(a1,b1, ncol=2)## Warning: Removed 450 rows containing non-finite values (stat_boxplot).dev.off()## svg
## 2Demography sub level
unique(final.df$demog)## [1] Estuary SS
## Levels: Estuary SSunique(final.df$demog_level)## [1] Est-Clines SS-Clines SS-Mtn
## Levels: SS-Clines < SS-Mtn < Est-Clinesunique(final.df$demog_level_sub)## [1] N central cline\nm constant N latitude cline\nm constant
## [3] N equal\nm breaks N equal\nm constant
## [5] N variable\nm variable
## 5 Levels: N equal\nm constant < ... < N variable\nm variablepleiotropy.df$demog_level_sub <- as.factor(pleiotropy.df$demog_level_sub)
levels(pleiotropy.df$demog_level_sub) <- c("N equal\nm constant", "N equal\nm breaks", "N latitude cline\nm constant", "N central cline\nm constant", "N variable\nm variable") # make sure in correct order
## Sub demograph - mako color
d1 <- ggplot(pleiotropy.df, aes(x=as.factor(demog_level_sub), y=cor_TPR, fill=as.factor(demog_level_sub))) + geom_boxplot(color="grey") + ggtheme + xlab("Demography") + ylab("Proportion of causal loci\nwith significant clines") + scale_fill_viridis(option="mako",discrete=TRUE) + theme(legend.position = "none") + theme( axis.text.x=element_text(angle=90,vjust=0.5, hjust=1, size = 10)) + ggtitle("A")
d1
## Strength of selection - magma color
my_col_S <- c(magma(10)[5], magma(10)[8])
s1<- ggplot(subdf_arch, aes(x=arch_level, y=cor_TPR_temp, fill=as.factor(SIGMA_K_2))) + geom_boxplot(color="grey") + ggtheme + xlab("") + ylab("Proportion of causal temp. \n loci with significant clines") + scale_fill_manual( values=my_col_S, labels=c(
expression(paste("Strong selection ", sigma[K]," = 0.5")),
expression(paste("Weak selection ", sigma[K]," = 4.0"))
), name="2-trait architectures") + ylim(0,1) + xlab("Genetic architecture") + ggtitle("B")
s1
pdf(paste0(outputs,"WhatDrivesNonClines-subdemog-selection.pdf"), width=6, height=8)
grid.arrange(d1, s1, nrow=2)
dev.off()## svg
## 2Does hanging strength of selection on trait 1 (temp) affect the number of allee frequency clines that evolve in trait 2 (env2)? Graph suggests no.
#+ theme(legend.title=element_text("2-trait\narchitectures"))
ggplot(subdf_arch, aes(x=arch_level, y=cor_TPR_sal, fill=as.factor(SIGMA_K_2))) + geom_boxplot(color="grey") + ggtheme + xlab("") + ylab("Proportion of causal sal. \n loci with significant clines") + scale_fill_manual( values=my_col_S, labels=c("Strong selection on other trait (sigma_K=0.5)", "Weak selection on other trait (sigma_K=4)"), name="2-trait architectures") + ylim(0,1) #+ theme(legend.title=element_text("2-trait\narchitectures"))
Distribution of the correlation between structure and deme environment
sum(is.na(final.df$cor_PC1_temp))## [1] 0# First PC axis with temp
sc1<- ggplot(final.df) + geom_histogram(aes(x=abs(cor_PC1_temp), fill=demog_level_sub), binwidth=0.05, color="grey") + ggtheme + coord_cartesian(xlim = c(0, 1)) + facet_grid(demog_level~., scales = "free_y") + scale_fill_viridis(option="mako", discrete=TRUE, name="Demography level") + xlab("|Cor(PC1, deme Temperature)|") + ylab("Number of simulations") + theme(legend.position="none") + ggtitle("A) Temperature")
sc1
# 2nd PC axis with temp
sc2<-ggplot(final.df) + geom_histogram(aes(x=abs(cor_PC2_temp), fill=demog_level_sub), binwidth=0.05, color="grey") + ggtheme + coord_cartesian(xlim = c(0, 1)) + facet_grid(demog_level~., scales = "free_y") + scale_fill_viridis(option="mako", discrete=TRUE, name="Demography level") + xlab("|Cor(PC2, deme temp)|") + ylab("Number of simulations") + theme(legend.position="none")
sc2
# 1st PC axis with Env2
sc3<-ggplot(final.df) + geom_histogram(aes(x=abs(cor_PC1_sal), fill=demog_level_sub), binwidth=0.05, color="grey") + ggtheme + coord_cartesian(xlim = c(0, 1)) + facet_grid(demog_level~., scales = "free_y") + scale_fill_viridis(option="mako", discrete=TRUE, name="Demography") + xlab("|Cor(PC1, deme Env2)|") + ylab("Number of simulations") + ggtitle("B) Env2") + theme(legend.position="right")
sc3
# 2nd PC axis with Env2
sc4<-ggplot(final.df) + geom_histogram(aes(x=abs(cor_PC2_sal), fill=demog_level_sub), binwidth=0.05, color="grey") + ggtheme + coord_cartesian(xlim = c(0, 1)) + facet_grid(demog_level~., scales = "free_y") + scale_fill_viridis(option="mako", discrete=TRUE, name="Demography level") + xlab("|Cor(PC2, deme Env2)|") + ylab("Number of simulations") + theme(legend.position="bottom")
sc4
pdf(paste0(outputs,"StructureEnvCor_facetDemog.pdf"), width=12, height=9)
grid.arrange(sc1, sc3, layout_matrix=matrix(c(1,1,1,2,2,2,2),
nrow=1), nrow=1)
dev.off()## svg
## 2Under what conditions can we reliably identify the alleles with effects on adaptive phenotypes using genetic-environment associations (latent factor mixed models, uncorrected correlations, and redundancy analysis)?
Compare methods AUC
AUC stands for area under the curve, here I use the Precision-Recall curve
# Check for missing data
sum(is.na(final.df$cor_AUCPR_temp_neutSNPs))## [1] 0sum(is.na(final.df$cor_AUCPR_sal_neutSNPs)) #expected 450 missing## [1] 450sum(is.na(final.df$LEA3.2_lfmm2_AUCPR_temp_neutSNPs))## [1] 0sum(is.na(final.df$LEA3.2_lfmm2_AUCPR_sal_neutSNPs)) #expected 450 missing## [1] 450sum(is.na(final.df$RDA_AUCPR_neutSNPs))## [1] 0sum(is.na(final.df$RDA_AUCPR_neutSNPs_corr))## [1] 0# Reorganize data for plotting
comparemethods.AUC <- gather(final.df, key=method, value=AUCPR, cor_AUCPR_temp_neutSNPs, LEA3.2_lfmm2_AUCPR_temp_neutSNPs, RDA_AUCPR_neutSNPs, RDA_AUCPR_neutSNPs_corr, cor_AUCPR_sal_neutSNPs,
LEA3.2_lfmm2_AUCPR_sal_neutSNPs)
levels(factor(comparemethods.AUC$method))## [1] "cor_AUCPR_sal_neutSNPs" "cor_AUCPR_temp_neutSNPs"
## [3] "LEA3.2_lfmm2_AUCPR_sal_neutSNPs" "LEA3.2_lfmm2_AUCPR_temp_neutSNPs"
## [5] "RDA_AUCPR_neutSNPs" "RDA_AUCPR_neutSNPs_corr"comparemethods.AUC$methodtype <- factor(comparemethods.AUC$method)
comparemethods.AUC$methodtype <-
revalue(comparemethods.AUC$methodtype,
c(
"cor_AUCPR_temp_neutSNPs"= "Cor(p, Temp.)",
"LEA3.2_lfmm2_AUCPR_temp_neutSNPs" = "LFMM - Temp\n(structure correction)",
"cor_AUCPR_sal_neutSNPs"="Cor(p, Env2)",
"LEA3.2_lfmm2_AUCPR_sal_neutSNPs" = "LFMM - Env2\n(structure correction)",
"RDA_AUCPR_neutSNPs" = "RDA",
"RDA_AUCPR_neutSNPs_corr" = "pRDA\n(structure correction)" ))
comparemethods.AUC$methodtype <- factor(comparemethods.AUC$methodtype,
levels = c("Cor(p, Temp.)",
"LFMM - Temp\n(structure correction)",
"Cor(p, Env2)",
"LFMM - Env2\n(structure correction)",
"RDA",
"pRDA\n(structure correction)"),
ordered=TRUE)
# Sanity checks
levels(comparemethods.AUC$methodtype)## [1] "Cor(p, Temp.)" "LFMM - Temp\n(structure correction)"
## [3] "Cor(p, Env2)" "LFMM - Env2\n(structure correction)"
## [5] "RDA" "pRDA\n(structure correction)"table(comparemethods.AUC$methodtype)##
## Cor(p, Temp.) LFMM - Temp\n(structure correction)
## 2250 2250
## Cor(p, Env2) LFMM - Env2\n(structure correction)
## 2250 2250
## RDA pRDA\n(structure correction)
## 2250 2250summary(comparemethods.AUC$AUCPR)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0314 0.0890 0.2013 0.2791 1.0000 900# Across all simulations, AUCPR
g <- ggplot(comparemethods.AUC, aes(x=as.factor(methodtype), y=AUCPR)) + geom_boxplot(color="grey", fill=rep(c("darkblue", "cornflowerblue"),3)) + ggtheme + xlab("method") + ylab("AUC-PR\n(worse <<--->> better)") + theme( axis.text.x=element_text(angle=90,vjust=0.5, hjust=1, size = 10)) + scale_color_viridis(name="Landscape", discrete=TRUE) + guides(fill=guide_legend(title="Landscape")) +ggtitle("A")
g## Warning: Removed 900 rows containing non-finite values (stat_boxplot).
# tried a violin plot, but the boxplot is better
# 450 NAs are expected for the Env2 models
tapply(comparemethods.AUC$AUCPR,comparemethods.AUC$methodtype, function(x){sum(is.na(x))})## Cor(p, Temp.) LFMM - Temp\n(structure correction)
## 0 0
## Cor(p, Env2) LFMM - Env2\n(structure correction)
## 450 450
## RDA pRDA\n(structure correction)
## 0 0Compare methods FDR
False discovery rate (FDR) is the proportion of positive tests that are true positives. If there are no positive tests, then the statistic is undefined (NA).
#Check for NAs in temperature models
sum(is.na(final.df$cor_FDR_neutSNPs_temp))## [1] 0 sum(is.na(final.df$LEA3.2_lfmm2_FDR_neutSNPs_temp))## [1] 697 # A lot of NAs here
# Check NAs in FDR or temp/salinity model come from cases with 0 outliers
table(isFDR_NA=is.na(final.df$LEA3.2_lfmm2_FDR_neutSNPs_temp),
is_0_outliers=(final.df$LEA3.2_lfmm2_num_causal_sig_temp==0 &
final.df$LEA3.2_lfmm2_num_neut_sig_temp==0))## is_0_outliers
## isFDR_NA FALSE TRUE
## FALSE 1553 0
## TRUE 0 697 table(isFDR_NA=is.na(final.df$LEA3.2_lfmm2_FDR_neutSNPs_sal),
is_0_outliers=(final.df$LEA3.2_lfmm2_num_causal_sig_sal==0 &
final.df$LEA3.2_lfmm2_num_neut_sig_sal==0))## is_0_outliers
## isFDR_NA FALSE TRUE
## FALSE 1689 0
## TRUE 0 561 tapply(is.na(final.df$LEA3.2_lfmm2_FDR_neutSNPs_temp),
list(final.df$demog_level, final.df$demog_level_sub), sum)## N equal\nm constant N equal\nm breaks N latitude cline\nm constant
## SS-Clines 40 41 62
## SS-Mtn 45 63 55
## Est-Clines 31 34 54
## N central cline\nm constant N variable\nm variable
## SS-Clines 50 53
## SS-Mtn 56 58
## Est-Clines 9 46 ## NAs are evenly spread across the demographies, but slightly higher in the SS scenarios
#Check for NAs in RDA model
sum(is.na(final.df$RDA_FDR_neutSNPs))## [1] 2 sum(is.na(final.df$RDA_FDR_neutSNPs_corr))## [1] 0# reorganize data for plotting
comparemethods.FDR <- gather(final.df, key=method, value=FDR, cor_FDR_neutSNPs_temp, LEA3.2_lfmm2_FDR_neutSNPs_temp, RDA_FDR_neutSNPs, RDA_FDR_neutSNPs_corr, cor_FDR_neutSNPs_sal, LEA3.2_lfmm2_FDR_neutSNPs_sal)
comparemethods.FDR <- comparemethods.FDR[order(comparemethods.FDR$seed),]
tail(comparemethods.FDR)## seed
## 2250 1233343
## 4500 1233343
## 6750 1233343
## 9000 1233343
## 11250 1233343
## 13500 1233343
## level reps
## 2250 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 4500 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 6750 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 9000 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 11250 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 13500 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## arch demog_name
## 2250 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 4500 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 6750 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 9000 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 11250 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 13500 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## demog_level_sub demog_level MIG_x MIG_y xcline ycline demog
## 2250 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 4500 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 6750 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 9000 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 11250 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 13500 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## METAPOP_SIDE_x METAPOP_SIDE_y Nequal isVariableM MIG_breaks
## 2250 10 10 3 1 0
## 4500 10 10 3 1 0
## 6750 10 10 3 1 0
## 9000 10 10 3 1 0
## 11250 10 10 3 1 0
## 13500 10 10 3 1 0
## arch_level_sub arch_level MU_base MU_QTL_proportion
## 2250 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 4500 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 6750 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 9000 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 11250 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 13500 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## SIGMA_QTN_1 SIGMA_QTN_2 SIGMA_K_1 SIGMA_K_2 N_traits ispleiotropy
## 2250 0.4 0.4 0.5 4 2 Pleiotropy
## 4500 0.4 0.4 0.5 4 2 Pleiotropy
## 6750 0.4 0.4 0.5 4 2 Pleiotropy
## 9000 0.4 0.4 0.5 4 2 Pleiotropy
## 11250 0.4 0.4 0.5 4 2 Pleiotropy
## 13500 0.4 0.4 0.5 4 2 Pleiotropy
## n_samp_tot n_samp_per_pop sd_fitness_among_inds sd_fitness_among_pops
## 2250 1000 10 0.1193353 0.0675856
## 4500 1000 10 0.1193353 0.0675856
## 6750 1000 10 0.1193353 0.0675856
## 9000 1000 10 0.1193353 0.0675856
## 11250 1000 10 0.1193353 0.0675856
## 13500 1000 10 0.1193353 0.0675856
## final_LA K Bonf_alpha numCausalLowMAFsample all_corr_phen_temp
## 2250 0.361984 1 6.179706e-06 0 0.8163458
## 4500 0.361984 1 6.179706e-06 0 0.8163458
## 6750 0.361984 1 6.179706e-06 0 0.8163458
## 9000 0.361984 1 6.179706e-06 0 0.8163458
## 11250 0.361984 1 6.179706e-06 0 0.8163458
## 13500 0.361984 1 6.179706e-06 0 0.8163458
## subsamp_corr_phen_temp all_corr_phen_sal subsamp_corr_phen_sal
## 2250 0.6347974 0.884965 0.7608153
## 4500 0.6347974 0.884965 0.7608153
## 6750 0.6347974 0.884965 0.7608153
## 9000 0.6347974 0.884965 0.7608153
## 11250 0.6347974 0.884965 0.7608153
## 13500 0.6347974 0.884965 0.7608153
## num_causal_prefilter num_causal_postfilter num_non_causal
## 2250 12 8 8083
## 4500 12 8 8083
## 6750 12 8 8083
## 9000 12 8 8083
## 11250 12 8 8083
## 13500 12 8 8083
## num_neut_prefilter num_neut_postfilter num_neut_neutralgenome
## 2250 8197 8197 3947
## 4500 8197 8197 3947
## 6750 8197 8197 3947
## 9000 8197 8197 3947
## 11250 8197 8197 3947
## 13500 8197 8197 3947
## num_causal_temp num_causal_sal num_multiallelic meanFst va_temp_total
## 2250 8 8 0 0.1737941 0.08556485
## 4500 8 8 0 0.1737941 0.08556485
## 6750 8 8 0 0.1737941 0.08556485
## 9000 8 8 0 0.1737941 0.08556485
## 11250 8 8 0 0.1737941 0.08556485
## 13500 8 8 0 0.1737941 0.08556485
## va_sal_total Va_temp_sample Va_sal_sample nSNPs median_causal_temp_cor
## 2250 0.105924 0.08049695 0.09935857 8091 0.3033895
## 4500 0.105924 0.08049695 0.09935857 8091 0.3033895
## 6750 0.105924 0.08049695 0.09935857 8091 0.3033895
## 9000 0.105924 0.08049695 0.09935857 8091 0.3033895
## 11250 0.105924 0.08049695 0.09935857 8091 0.3033895
## 13500 0.105924 0.08049695 0.09935857 8091 0.3033895
## median_causal_sal_cor median_neut_temp_cor median_neut_sal_cor
## 2250 0.3424647 0.248889 0.1358742
## 4500 0.3424647 0.248889 0.1358742
## 6750 0.3424647 0.248889 0.1358742
## 9000 0.3424647 0.248889 0.1358742
## 11250 0.3424647 0.248889 0.1358742
## 13500 0.3424647 0.248889 0.1358742
## cor_VA_temp_prop cor_VA_sal_prop cor_TPR_temp cor_TPR_sal
## 2250 0.3901235 0.8777962 0.375 0.5
## 4500 0.3901235 0.8777962 0.375 0.5
## 6750 0.3901235 0.8777962 0.375 0.5
## 9000 0.3901235 0.8777962 0.375 0.5
## 11250 0.3901235 0.8777962 0.375 0.5
## 13500 0.3901235 0.8777962 0.375 0.5
## cor_FDR_allSNPs_temp cor_FDR_allSNPs_sal num_causal_sig_temp_corr
## 2250 0.9987903 0.9913043 3
## 4500 0.9987903 0.9913043 3
## 6750 0.9987903 0.9913043 3
## 9000 0.9987903 0.9913043 3
## 11250 0.9987903 0.9913043 3
## 13500 0.9987903 0.9913043 3
## num_causal_sig_sal_corr num_notCausal_sig_temp_corr
## 2250 4 2477
## 4500 4 2477
## 6750 4 2477
## 9000 4 2477
## 11250 4 2477
## 13500 4 2477
## num_notCausal_sig_sal_corr num_neut_sig_temp_corr num_neut_sig_sal_corr
## 2250 456 1195 152
## 4500 456 1195 152
## 6750 456 1195 152
## 9000 456 1195 152
## 11250 456 1195 152
## 13500 456 1195 152
## cor_AUCPR_temp_allSNPs cor_AUCPR_temp_neutSNPs cor_AUCPR_sal_allSNPs
## 2250 0.001387016 0.002938006 0.02439708
## 4500 0.001387016 0.002938006 0.02439708
## 6750 0.001387016 0.002938006 0.02439708
## 9000 0.001387016 0.002938006 0.02439708
## 11250 0.001387016 0.002938006 0.02439708
## 13500 0.001387016 0.002938006 0.02439708
## cor_AUCPR_sal_neutSNPs cor_af_temp_noutliers cor_af_sal_noutliers
## 2250 0.2860155 2480 460
## 4500 0.2860155 2480 460
## 6750 0.2860155 2480 460
## 9000 0.2860155 2480 460
## 11250 0.2860155 2480 460
## 13500 0.2860155 2480 460
## cor_FPR_temp_neutSNPs cor_FPR_sal_neutSNPs LEA3.2_lfmm2_Va_temp_prop
## 2250 0.3027616 0.03851026 0
## 4500 0.3027616 0.03851026 0
## 6750 0.3027616 0.03851026 0
## 9000 0.3027616 0.03851026 0
## 11250 0.3027616 0.03851026 0
## 13500 0.3027616 0.03851026 0
## LEA3.2_lfmm2_Va_sal_prop LEA3.2_lfmm2_TPR_temp LEA3.2_lfmm2_TPR_sal
## 2250 0.8777962 0 0.5
## 4500 0.8777962 0 0.5
## 6750 0.8777962 0 0.5
## 9000 0.8777962 0 0.5
## 11250 0.8777962 0 0.5
## 13500 0.8777962 0 0.5
## LEA3.2_lfmm2_FDR_allSNPs_temp LEA3.2_lfmm2_FDR_allSNPs_sal
## 2250 NA 0.9090909
## 4500 NA 0.9090909
## 6750 NA 0.9090909
## 9000 NA 0.9090909
## 11250 NA 0.9090909
## 13500 NA 0.9090909
## LEA3.2_lfmm2_AUCPR_temp_allSNPs LEA3.2_lfmm2_AUCPR_temp_neutSNPs
## 2250 0.2117759 0.5088647
## 4500 0.2117759 0.5088647
## 6750 0.2117759 0.5088647
## 9000 0.2117759 0.5088647
## 11250 0.2117759 0.5088647
## 13500 0.2117759 0.5088647
## LEA3.2_lfmm2_AUCPR_sal_allSNPs LEA3.2_lfmm2_AUCPR_sal_neutSNPs
## 2250 0.2117759 0.5088647
## 4500 0.2117759 0.5088647
## 6750 0.2117759 0.5088647
## 9000 0.2117759 0.5088647
## 11250 0.2117759 0.5088647
## 13500 0.2117759 0.5088647
## LEA3.2_lfmm2_mlog10P_tempenv_noutliers
## 2250 0
## 4500 0
## 6750 0
## 9000 0
## 11250 0
## 13500 0
## LEA3.2_lfmm2_mlog10P_salenv_noutliers LEA3.2_lfmm2_num_causal_sig_temp
## 2250 44 0
## 4500 44 0
## 6750 44 0
## 9000 44 0
## 11250 44 0
## 13500 44 0
## LEA3.2_lfmm2_num_neut_sig_temp LEA3.2_lfmm2_num_causal_sig_sal
## 2250 0 4
## 4500 0 4
## 6750 0 4
## 9000 0 4
## 11250 0 4
## 13500 0 4
## LEA3.2_lfmm2_num_neut_sig_sal LEA3.2_lfmm2_FPR_neutSNPs_temp
## 2250 0 0
## 4500 0 0
## 6750 0 0
## 9000 0 0
## 11250 0 0
## 13500 0 0
## LEA3.2_lfmm2_FPR_neutSNPs_sal RDA1_propvar RDA2_propvar RDA1_propvar_corr
## 2250 0 0.739 0.261 0.673
## 4500 0 0.739 0.261 0.673
## 6750 0 0.739 0.261 0.673
## 9000 0 0.739 0.261 0.673
## 11250 0 0.739 0.261 0.673
## 13500 0 0.739 0.261 0.673
## RDA2_propvar_corr RDA1_temp_cor RDA1_sal_cor RDA2_temp_cor RDA2_sal_cor
## 2250 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 4500 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 6750 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 9000 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 11250 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 13500 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## RDA_Va_temp_prop RDA_Va_temp_prop_corr RDA_Va_sal_prop
## 2250 0.8073374 0.8955353 0.8777962
## 4500 0.8073374 0.8955353 0.8777962
## 6750 0.8073374 0.8955353 0.8777962
## 9000 0.8073374 0.8955353 0.8777962
## 11250 0.8073374 0.8955353 0.8777962
## 13500 0.8073374 0.8955353 0.8777962
## RDA_Va_sal_prop_corr RDA_TPR RDA_TPR_corr RDA_FDR_allSNPs
## 2250 0.9096928 0.5 0.625 0.983871
## 4500 0.9096928 0.5 0.625 0.983871
## 6750 0.9096928 0.5 0.625 0.983871
## 9000 0.9096928 0.5 0.625 0.983871
## 11250 0.9096928 0.5 0.625 0.983871
## 13500 0.9096928 0.5 0.625 0.983871
## RDA_FDR_allSNPs_corr num_RDA_sig_causal num_RDA_sig_neutral
## 2250 0.9814815 4 62
## 4500 0.9814815 4 62
## 6750 0.9814815 4 62
## 9000 0.9814815 4 62
## 11250 0.9814815 4 62
## 13500 0.9814815 4 62
## num_RDA_sig_causal_corr num_RDA_sig_neutral_corr RDA_AUCPR_allSNPs
## 2250 5 92 0.203218
## 4500 5 92 0.203218
## 6750 5 92 0.203218
## 9000 5 92 0.203218
## 11250 5 92 0.203218
## 13500 5 92 0.203218
## RDA_AUCPR_neutSNPs RDA_AUCPR_neutSNPs_corr RDA_FPR_neutSNPs
## 2250 0.5029961 0.332056 0.01570813
## 4500 0.5029961 0.332056 0.01570813
## 6750 0.5029961 0.332056 0.01570813
## 9000 0.5029961 0.332056 0.01570813
## 11250 0.5029961 0.332056 0.01570813
## 13500 0.5029961 0.332056 0.01570813
## RDA_FPR_neutSNPs_corr RDA_RDAmutpred_cor_tempEffect
## 2250 0.02330884 0.5
## 4500 0.02330884 0.5
## 6750 0.02330884 0.5
## 9000 0.02330884 0.5
## 11250 0.02330884 0.5
## 13500 0.02330884 0.5
## RDA_RDAmutpred_cor_salEffect RDA_absRDAmutpred_cor_tempVa
## 2250 0.2857143 0.02069674
## 4500 0.2857143 0.02069674
## 6750 0.2857143 0.02069674
## 9000 0.2857143 0.02069674
## 11250 0.2857143 0.02069674
## 13500 0.2857143 0.02069674
## RDA_absRDAmutpred_cor_salVa RDA_RDAmutpred_cor_tempEffect_structcorr
## 2250 0.02400657 0.2752112
## 4500 0.02400657 0.2752112
## 6750 0.02400657 0.2752112
## 9000 0.02400657 0.2752112
## 11250 0.02400657 0.2752112
## 13500 0.02400657 0.2752112
## RDA_RDAmutpred_cor_salEffect_structcorr
## 2250 0.6602084
## 4500 0.6602084
## 6750 0.6602084
## 9000 0.6602084
## 11250 0.6602084
## 13500 0.6602084
## RDA_absRDAmutpred_cor_tempVa_structcorr
## 2250 0.08556552
## 4500 0.08556552
## 6750 0.08556552
## 9000 0.08556552
## 11250 0.08556552
## 13500 0.08556552
## RDA_absRDAmutpred_cor_salVa_structcorr
## 2250 0.1153435
## 4500 0.1153435
## 6750 0.1153435
## 9000 0.1153435
## 11250 0.1153435
## 13500 0.1153435
## RDA_cor_RDA20000temppredict_tempPhen RDA_cor_RDA20000salpredict_salPhen
## 2250 0.5661217 0.6653819
## 4500 0.5661217 0.6653819
## 6750 0.5661217 0.6653819
## 9000 0.5661217 0.6653819
## 11250 0.5661217 0.6653819
## 13500 0.5661217 0.6653819
## RDA_cor_RDA20000temppredict_tempPhen_structcorr
## 2250 0.238542
## 4500 0.238542
## 6750 0.238542
## 9000 0.238542
## 11250 0.238542
## 13500 0.238542
## RDA_cor_RDA20000salpredict_salPhen_structcorr cor_PC1_temp cor_PC1_sal
## 2250 0.6262283 0.7832223 0.09915203
## 4500 0.6262283 0.7832223 0.09915203
## 6750 0.6262283 0.7832223 0.09915203
## 9000 0.6262283 0.7832223 0.09915203
## 11250 0.6262283 0.7832223 0.09915203
## 13500 0.6262283 0.7832223 0.09915203
## cor_PC2_temp cor_PC2_sal cor_LFMMU1_temp cor_LFMMU1_sal cor_LFMMU2_temp
## 2250 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 4500 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 6750 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 9000 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 11250 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 13500 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## cor_LFMMU2_sal cor_PC1_LFMMU1_temp cor_PC1_LFMMU1_sal cor_PC2_LFMMU1_temp
## 2250 NA 0.8090855 -0.9986383 0.5807664
## 4500 NA 0.8090855 -0.9986383 0.5807664
## 6750 NA 0.8090855 -0.9986383 0.5807664
## 9000 NA 0.8090855 -0.9986383 0.5807664
## 11250 NA 0.8090855 -0.9986383 0.5807664
## 13500 NA 0.8090855 -0.9986383 0.5807664
## cor_PC2_LFMMU1_sal gwas_TPR_sal gwas_TPR_temp gwas_FDR_sal_neutbase
## 2250 -0.02590842 0.75 0.875 0.997544
## 4500 -0.02590842 0.75 0.875 0.997544
## 6750 -0.02590842 0.75 0.875 0.997544
## 9000 -0.02590842 0.75 0.875 0.997544
## 11250 -0.02590842 0.75 0.875 0.997544
## 13500 -0.02590842 0.75 0.875 0.997544
## gwas_FDR_temp_neutbase clinalparadigm_sal_proptop5GWASclines
## 2250 0.996434 0.5333333
## 4500 0.996434 0.5333333
## 6750 0.996434 0.5333333
## 9000 0.996434 0.5333333
## 11250 0.996434 0.5333333
## 13500 0.996434 0.5333333
## clinalparadigm_temp_proptop5GWASclines
## 2250 0.2074074
## 4500 0.2074074
## 6750 0.2074074
## 9000 0.2074074
## 11250 0.2074074
## 13500 0.2074074
## clinalparadigm_sal_propsigGWASclines
## 2250 0.09349593
## 4500 0.09349593
## 6750 0.09349593
## 9000 0.09349593
## 11250 0.09349593
## 13500 0.09349593
## clinalparadigm_temp_propsigGWASclines N_traits2
## 2250 0.2850657 2 Traits
## 4500 0.2850657 2 Traits
## 6750 0.2850657 2 Traits
## 9000 0.2850657 2 Traits
## 11250 0.2850657 2 Traits
## 13500 0.2850657 2 Traits
## method FDR
## 2250 cor_FDR_neutSNPs_temp 0.9974958
## 4500 LEA3.2_lfmm2_FDR_neutSNPs_temp NA
## 6750 RDA_FDR_neutSNPs 0.9393939
## 9000 RDA_FDR_neutSNPs_corr 0.9484536
## 11250 cor_FDR_neutSNPs_sal 0.9743590
## 13500 LEA3.2_lfmm2_FDR_neutSNPs_sal 0.0000000comparemethods.FDR$methodtype <- factor(comparemethods.FDR$method)
levels(comparemethods.FDR$methodtype)## [1] "cor_FDR_neutSNPs_sal" "cor_FDR_neutSNPs_temp"
## [3] "LEA3.2_lfmm2_FDR_neutSNPs_sal" "LEA3.2_lfmm2_FDR_neutSNPs_temp"
## [5] "RDA_FDR_neutSNPs" "RDA_FDR_neutSNPs_corr"comparemethods.FDR$methodtype <- revalue(comparemethods.FDR$methodtype,
c("cor_FDR_neutSNPs_sal"="Cor(p, Env2)",
"cor_FDR_neutSNPs_temp"= "Cor(p, Temp.)",
"LEA3.2_lfmm2_FDR_neutSNPs_sal" = "LFMM - Temp.\n(structure correction)",
"LEA3.2_lfmm2_FDR_neutSNPs_temp" = "LFMM - Env2\n(structure correction)",
"RDA_FDR_neutSNPs" = "RDA" ,
"RDA_FDR_neutSNPs_corr" = "pRDA\n(structure correction)" ))
comparemethods.FDR$methodtype <- factor(comparemethods.FDR$methodtype,
levels = c("Cor(p, Temp.)",
"LFMM - Temp.\n(structure correction)",
"Cor(p, Env2)",
"LFMM - Env2\n(structure correction)",
"RDA",
"pRDA\n(structure correction)"),
ordered=TRUE)
table(comparemethods.FDR$methodtype) ##
## Cor(p, Temp.) LFMM - Temp.\n(structure correction)
## 2250 2250
## Cor(p, Env2) LFMM - Env2\n(structure correction)
## 2250 2250
## RDA pRDA\n(structure correction)
## 2250 2250tapply(comparemethods.FDR$FDR,comparemethods.AUC$methodtype, function(x){sum(is.na(x))})## Cor(p, Temp.) LFMM - Temp\n(structure correction)
## 266 248
## Cor(p, Env2) LFMM - Env2\n(structure correction)
## 248 210
## RDA pRDA\n(structure correction)
## 204 246g2<- ggplot(comparemethods.FDR, aes(x=as.factor(methodtype), y=FDR)) + geom_boxplot(color="grey", fill=rep(c("darkblue", "cornflowerblue"),3)) + ggtheme + xlab("method") + ylab("FDR\n(better <<--->> worse)") + scale_fill_viridis(option="turbo", discrete=TRUE) + theme(legend.position = "none") + theme( axis.text.x=element_text(angle=60, vjust=1, hjust=1, size = 10)) + ggtitle("A")
g2## Warning: Removed 1422 rows containing non-finite values (stat_boxplot).
# There are more NAs because FDR has NAs for cases with no true positives (no power)Compare methods POWER
Ideally, we would like power to be the same after correcting for pop structure as before we correct.
#Check for NAs in temperature models
sum(is.na(final.df$cor_TPR_temp))## [1] 0 sum(is.na(final.df$LEA3.2_lfmm2_TPR_temp))## [1] 0# Check for NAs in salinity model
sum(is.na(final.df$cor_TPR_sal)) #450 expected## [1] 450 sum(is.na(final.df$LEA3.2_lfmm2_TPR_sal))## [1] 450#Check for NAs in RDA model
sum(is.na(final.df$RDA_TPR))## [1] 0 sum(is.na(final.df$RDA_TPR))## [1] 0# reorganize data for plotting
comparemethods.TPR <- gather(final.df, key=method, value=TPR, cor_TPR_temp, LEA3.2_lfmm2_TPR_temp, RDA_TPR, RDA_TPR_corr, cor_TPR_sal, LEA3.2_lfmm2_TPR_sal)
comparemethods.TPR <- comparemethods.TPR[order(comparemethods.TPR$seed),]
tail(comparemethods.TPR)## seed
## 2250 1233343
## 4500 1233343
## 6750 1233343
## 9000 1233343
## 11250 1233343
## 13500 1233343
## level reps
## 2250 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 4500 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 6750 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 9000 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 11250 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## 13500 oliogenic_2-trait-pleiotropy-unequal-S__SS-Mtn_N-variable_m-variable 10
## arch demog_name
## 2250 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 4500 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 6750 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 9000 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 11250 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## 13500 oliogenic_2-trait-pleiotropy-unequal-S SS-Mtn_N-variable_m-variable
## demog_level_sub demog_level MIG_x MIG_y xcline ycline demog
## 2250 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 4500 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 6750 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 9000 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 11250 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## 13500 N variable\nm variable SS-Mtn 0.03 0.03 V linear SS
## METAPOP_SIDE_x METAPOP_SIDE_y Nequal isVariableM MIG_breaks
## 2250 10 10 3 1 0
## 4500 10 10 3 1 0
## 6750 10 10 3 1 0
## 9000 10 10 3 1 0
## 11250 10 10 3 1 0
## 13500 10 10 3 1 0
## arch_level_sub arch_level MU_base MU_QTL_proportion
## 2250 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 4500 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 6750 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 9000 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 11250 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## 13500 2 traits\npleiotropy\nunequal S oligogenic 1e-07 0.001
## SIGMA_QTN_1 SIGMA_QTN_2 SIGMA_K_1 SIGMA_K_2 N_traits ispleiotropy
## 2250 0.4 0.4 0.5 4 2 Pleiotropy
## 4500 0.4 0.4 0.5 4 2 Pleiotropy
## 6750 0.4 0.4 0.5 4 2 Pleiotropy
## 9000 0.4 0.4 0.5 4 2 Pleiotropy
## 11250 0.4 0.4 0.5 4 2 Pleiotropy
## 13500 0.4 0.4 0.5 4 2 Pleiotropy
## n_samp_tot n_samp_per_pop sd_fitness_among_inds sd_fitness_among_pops
## 2250 1000 10 0.1193353 0.0675856
## 4500 1000 10 0.1193353 0.0675856
## 6750 1000 10 0.1193353 0.0675856
## 9000 1000 10 0.1193353 0.0675856
## 11250 1000 10 0.1193353 0.0675856
## 13500 1000 10 0.1193353 0.0675856
## final_LA K Bonf_alpha numCausalLowMAFsample all_corr_phen_temp
## 2250 0.361984 1 6.179706e-06 0 0.8163458
## 4500 0.361984 1 6.179706e-06 0 0.8163458
## 6750 0.361984 1 6.179706e-06 0 0.8163458
## 9000 0.361984 1 6.179706e-06 0 0.8163458
## 11250 0.361984 1 6.179706e-06 0 0.8163458
## 13500 0.361984 1 6.179706e-06 0 0.8163458
## subsamp_corr_phen_temp all_corr_phen_sal subsamp_corr_phen_sal
## 2250 0.6347974 0.884965 0.7608153
## 4500 0.6347974 0.884965 0.7608153
## 6750 0.6347974 0.884965 0.7608153
## 9000 0.6347974 0.884965 0.7608153
## 11250 0.6347974 0.884965 0.7608153
## 13500 0.6347974 0.884965 0.7608153
## num_causal_prefilter num_causal_postfilter num_non_causal
## 2250 12 8 8083
## 4500 12 8 8083
## 6750 12 8 8083
## 9000 12 8 8083
## 11250 12 8 8083
## 13500 12 8 8083
## num_neut_prefilter num_neut_postfilter num_neut_neutralgenome
## 2250 8197 8197 3947
## 4500 8197 8197 3947
## 6750 8197 8197 3947
## 9000 8197 8197 3947
## 11250 8197 8197 3947
## 13500 8197 8197 3947
## num_causal_temp num_causal_sal num_multiallelic meanFst va_temp_total
## 2250 8 8 0 0.1737941 0.08556485
## 4500 8 8 0 0.1737941 0.08556485
## 6750 8 8 0 0.1737941 0.08556485
## 9000 8 8 0 0.1737941 0.08556485
## 11250 8 8 0 0.1737941 0.08556485
## 13500 8 8 0 0.1737941 0.08556485
## va_sal_total Va_temp_sample Va_sal_sample nSNPs median_causal_temp_cor
## 2250 0.105924 0.08049695 0.09935857 8091 0.3033895
## 4500 0.105924 0.08049695 0.09935857 8091 0.3033895
## 6750 0.105924 0.08049695 0.09935857 8091 0.3033895
## 9000 0.105924 0.08049695 0.09935857 8091 0.3033895
## 11250 0.105924 0.08049695 0.09935857 8091 0.3033895
## 13500 0.105924 0.08049695 0.09935857 8091 0.3033895
## median_causal_sal_cor median_neut_temp_cor median_neut_sal_cor
## 2250 0.3424647 0.248889 0.1358742
## 4500 0.3424647 0.248889 0.1358742
## 6750 0.3424647 0.248889 0.1358742
## 9000 0.3424647 0.248889 0.1358742
## 11250 0.3424647 0.248889 0.1358742
## 13500 0.3424647 0.248889 0.1358742
## cor_VA_temp_prop cor_VA_sal_prop cor_FDR_allSNPs_temp
## 2250 0.3901235 0.8777962 0.9987903
## 4500 0.3901235 0.8777962 0.9987903
## 6750 0.3901235 0.8777962 0.9987903
## 9000 0.3901235 0.8777962 0.9987903
## 11250 0.3901235 0.8777962 0.9987903
## 13500 0.3901235 0.8777962 0.9987903
## cor_FDR_neutSNPs_temp cor_FDR_allSNPs_sal cor_FDR_neutSNPs_sal
## 2250 0.9974958 0.9913043 0.974359
## 4500 0.9974958 0.9913043 0.974359
## 6750 0.9974958 0.9913043 0.974359
## 9000 0.9974958 0.9913043 0.974359
## 11250 0.9974958 0.9913043 0.974359
## 13500 0.9974958 0.9913043 0.974359
## num_causal_sig_temp_corr num_causal_sig_sal_corr
## 2250 3 4
## 4500 3 4
## 6750 3 4
## 9000 3 4
## 11250 3 4
## 13500 3 4
## num_notCausal_sig_temp_corr num_notCausal_sig_sal_corr
## 2250 2477 456
## 4500 2477 456
## 6750 2477 456
## 9000 2477 456
## 11250 2477 456
## 13500 2477 456
## num_neut_sig_temp_corr num_neut_sig_sal_corr cor_AUCPR_temp_allSNPs
## 2250 1195 152 0.001387016
## 4500 1195 152 0.001387016
## 6750 1195 152 0.001387016
## 9000 1195 152 0.001387016
## 11250 1195 152 0.001387016
## 13500 1195 152 0.001387016
## cor_AUCPR_temp_neutSNPs cor_AUCPR_sal_allSNPs cor_AUCPR_sal_neutSNPs
## 2250 0.002938006 0.02439708 0.2860155
## 4500 0.002938006 0.02439708 0.2860155
## 6750 0.002938006 0.02439708 0.2860155
## 9000 0.002938006 0.02439708 0.2860155
## 11250 0.002938006 0.02439708 0.2860155
## 13500 0.002938006 0.02439708 0.2860155
## cor_af_temp_noutliers cor_af_sal_noutliers cor_FPR_temp_neutSNPs
## 2250 2480 460 0.3027616
## 4500 2480 460 0.3027616
## 6750 2480 460 0.3027616
## 9000 2480 460 0.3027616
## 11250 2480 460 0.3027616
## 13500 2480 460 0.3027616
## cor_FPR_sal_neutSNPs LEA3.2_lfmm2_Va_temp_prop LEA3.2_lfmm2_Va_sal_prop
## 2250 0.03851026 0 0.8777962
## 4500 0.03851026 0 0.8777962
## 6750 0.03851026 0 0.8777962
## 9000 0.03851026 0 0.8777962
## 11250 0.03851026 0 0.8777962
## 13500 0.03851026 0 0.8777962
## LEA3.2_lfmm2_FDR_allSNPs_temp LEA3.2_lfmm2_FDR_allSNPs_sal
## 2250 NA 0.9090909
## 4500 NA 0.9090909
## 6750 NA 0.9090909
## 9000 NA 0.9090909
## 11250 NA 0.9090909
## 13500 NA 0.9090909
## LEA3.2_lfmm2_FDR_neutSNPs_temp LEA3.2_lfmm2_FDR_neutSNPs_sal
## 2250 NA 0
## 4500 NA 0
## 6750 NA 0
## 9000 NA 0
## 11250 NA 0
## 13500 NA 0
## LEA3.2_lfmm2_AUCPR_temp_allSNPs LEA3.2_lfmm2_AUCPR_temp_neutSNPs
## 2250 0.2117759 0.5088647
## 4500 0.2117759 0.5088647
## 6750 0.2117759 0.5088647
## 9000 0.2117759 0.5088647
## 11250 0.2117759 0.5088647
## 13500 0.2117759 0.5088647
## LEA3.2_lfmm2_AUCPR_sal_allSNPs LEA3.2_lfmm2_AUCPR_sal_neutSNPs
## 2250 0.2117759 0.5088647
## 4500 0.2117759 0.5088647
## 6750 0.2117759 0.5088647
## 9000 0.2117759 0.5088647
## 11250 0.2117759 0.5088647
## 13500 0.2117759 0.5088647
## LEA3.2_lfmm2_mlog10P_tempenv_noutliers
## 2250 0
## 4500 0
## 6750 0
## 9000 0
## 11250 0
## 13500 0
## LEA3.2_lfmm2_mlog10P_salenv_noutliers LEA3.2_lfmm2_num_causal_sig_temp
## 2250 44 0
## 4500 44 0
## 6750 44 0
## 9000 44 0
## 11250 44 0
## 13500 44 0
## LEA3.2_lfmm2_num_neut_sig_temp LEA3.2_lfmm2_num_causal_sig_sal
## 2250 0 4
## 4500 0 4
## 6750 0 4
## 9000 0 4
## 11250 0 4
## 13500 0 4
## LEA3.2_lfmm2_num_neut_sig_sal LEA3.2_lfmm2_FPR_neutSNPs_temp
## 2250 0 0
## 4500 0 0
## 6750 0 0
## 9000 0 0
## 11250 0 0
## 13500 0 0
## LEA3.2_lfmm2_FPR_neutSNPs_sal RDA1_propvar RDA2_propvar RDA1_propvar_corr
## 2250 0 0.739 0.261 0.673
## 4500 0 0.739 0.261 0.673
## 6750 0 0.739 0.261 0.673
## 9000 0 0.739 0.261 0.673
## 11250 0 0.739 0.261 0.673
## 13500 0 0.739 0.261 0.673
## RDA2_propvar_corr RDA1_temp_cor RDA1_sal_cor RDA2_temp_cor RDA2_sal_cor
## 2250 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 4500 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 6750 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 9000 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 11250 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## 13500 0.327 0.9800515 0.1987435 -0.1987435 0.9800515
## RDA_Va_temp_prop RDA_Va_temp_prop_corr RDA_Va_sal_prop
## 2250 0.8073374 0.8955353 0.8777962
## 4500 0.8073374 0.8955353 0.8777962
## 6750 0.8073374 0.8955353 0.8777962
## 9000 0.8073374 0.8955353 0.8777962
## 11250 0.8073374 0.8955353 0.8777962
## 13500 0.8073374 0.8955353 0.8777962
## RDA_Va_sal_prop_corr RDA_FDR_allSNPs RDA_FDR_allSNPs_corr
## 2250 0.9096928 0.983871 0.9814815
## 4500 0.9096928 0.983871 0.9814815
## 6750 0.9096928 0.983871 0.9814815
## 9000 0.9096928 0.983871 0.9814815
## 11250 0.9096928 0.983871 0.9814815
## 13500 0.9096928 0.983871 0.9814815
## num_RDA_sig_causal num_RDA_sig_neutral num_RDA_sig_causal_corr
## 2250 4 62 5
## 4500 4 62 5
## 6750 4 62 5
## 9000 4 62 5
## 11250 4 62 5
## 13500 4 62 5
## num_RDA_sig_neutral_corr RDA_FDR_neutSNPs RDA_FDR_neutSNPs_corr
## 2250 92 0.9393939 0.9484536
## 4500 92 0.9393939 0.9484536
## 6750 92 0.9393939 0.9484536
## 9000 92 0.9393939 0.9484536
## 11250 92 0.9393939 0.9484536
## 13500 92 0.9393939 0.9484536
## RDA_AUCPR_allSNPs RDA_AUCPR_neutSNPs RDA_AUCPR_neutSNPs_corr
## 2250 0.203218 0.5029961 0.332056
## 4500 0.203218 0.5029961 0.332056
## 6750 0.203218 0.5029961 0.332056
## 9000 0.203218 0.5029961 0.332056
## 11250 0.203218 0.5029961 0.332056
## 13500 0.203218 0.5029961 0.332056
## RDA_FPR_neutSNPs RDA_FPR_neutSNPs_corr RDA_RDAmutpred_cor_tempEffect
## 2250 0.01570813 0.02330884 0.5
## 4500 0.01570813 0.02330884 0.5
## 6750 0.01570813 0.02330884 0.5
## 9000 0.01570813 0.02330884 0.5
## 11250 0.01570813 0.02330884 0.5
## 13500 0.01570813 0.02330884 0.5
## RDA_RDAmutpred_cor_salEffect RDA_absRDAmutpred_cor_tempVa
## 2250 0.2857143 0.02069674
## 4500 0.2857143 0.02069674
## 6750 0.2857143 0.02069674
## 9000 0.2857143 0.02069674
## 11250 0.2857143 0.02069674
## 13500 0.2857143 0.02069674
## RDA_absRDAmutpred_cor_salVa RDA_RDAmutpred_cor_tempEffect_structcorr
## 2250 0.02400657 0.2752112
## 4500 0.02400657 0.2752112
## 6750 0.02400657 0.2752112
## 9000 0.02400657 0.2752112
## 11250 0.02400657 0.2752112
## 13500 0.02400657 0.2752112
## RDA_RDAmutpred_cor_salEffect_structcorr
## 2250 0.6602084
## 4500 0.6602084
## 6750 0.6602084
## 9000 0.6602084
## 11250 0.6602084
## 13500 0.6602084
## RDA_absRDAmutpred_cor_tempVa_structcorr
## 2250 0.08556552
## 4500 0.08556552
## 6750 0.08556552
## 9000 0.08556552
## 11250 0.08556552
## 13500 0.08556552
## RDA_absRDAmutpred_cor_salVa_structcorr
## 2250 0.1153435
## 4500 0.1153435
## 6750 0.1153435
## 9000 0.1153435
## 11250 0.1153435
## 13500 0.1153435
## RDA_cor_RDA20000temppredict_tempPhen RDA_cor_RDA20000salpredict_salPhen
## 2250 0.5661217 0.6653819
## 4500 0.5661217 0.6653819
## 6750 0.5661217 0.6653819
## 9000 0.5661217 0.6653819
## 11250 0.5661217 0.6653819
## 13500 0.5661217 0.6653819
## RDA_cor_RDA20000temppredict_tempPhen_structcorr
## 2250 0.238542
## 4500 0.238542
## 6750 0.238542
## 9000 0.238542
## 11250 0.238542
## 13500 0.238542
## RDA_cor_RDA20000salpredict_salPhen_structcorr cor_PC1_temp cor_PC1_sal
## 2250 0.6262283 0.7832223 0.09915203
## 4500 0.6262283 0.7832223 0.09915203
## 6750 0.6262283 0.7832223 0.09915203
## 9000 0.6262283 0.7832223 0.09915203
## 11250 0.6262283 0.7832223 0.09915203
## 13500 0.6262283 0.7832223 0.09915203
## cor_PC2_temp cor_PC2_sal cor_LFMMU1_temp cor_LFMMU1_sal cor_LFMMU2_temp
## 2250 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 4500 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 6750 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 9000 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 11250 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## 13500 -0.4132312 -0.2945365 0.3684028 -0.0665779 NA
## cor_LFMMU2_sal cor_PC1_LFMMU1_temp cor_PC1_LFMMU1_sal cor_PC2_LFMMU1_temp
## 2250 NA 0.8090855 -0.9986383 0.5807664
## 4500 NA 0.8090855 -0.9986383 0.5807664
## 6750 NA 0.8090855 -0.9986383 0.5807664
## 9000 NA 0.8090855 -0.9986383 0.5807664
## 11250 NA 0.8090855 -0.9986383 0.5807664
## 13500 NA 0.8090855 -0.9986383 0.5807664
## cor_PC2_LFMMU1_sal gwas_TPR_sal gwas_TPR_temp gwas_FDR_sal_neutbase
## 2250 -0.02590842 0.75 0.875 0.997544
## 4500 -0.02590842 0.75 0.875 0.997544
## 6750 -0.02590842 0.75 0.875 0.997544
## 9000 -0.02590842 0.75 0.875 0.997544
## 11250 -0.02590842 0.75 0.875 0.997544
## 13500 -0.02590842 0.75 0.875 0.997544
## gwas_FDR_temp_neutbase clinalparadigm_sal_proptop5GWASclines
## 2250 0.996434 0.5333333
## 4500 0.996434 0.5333333
## 6750 0.996434 0.5333333
## 9000 0.996434 0.5333333
## 11250 0.996434 0.5333333
## 13500 0.996434 0.5333333
## clinalparadigm_temp_proptop5GWASclines
## 2250 0.2074074
## 4500 0.2074074
## 6750 0.2074074
## 9000 0.2074074
## 11250 0.2074074
## 13500 0.2074074
## clinalparadigm_sal_propsigGWASclines
## 2250 0.09349593
## 4500 0.09349593
## 6750 0.09349593
## 9000 0.09349593
## 11250 0.09349593
## 13500 0.09349593
## clinalparadigm_temp_propsigGWASclines N_traits2 method
## 2250 0.2850657 2 Traits cor_TPR_temp
## 4500 0.2850657 2 Traits LEA3.2_lfmm2_TPR_temp
## 6750 0.2850657 2 Traits RDA_TPR
## 9000 0.2850657 2 Traits RDA_TPR_corr
## 11250 0.2850657 2 Traits cor_TPR_sal
## 13500 0.2850657 2 Traits LEA3.2_lfmm2_TPR_sal
## TPR
## 2250 0.375
## 4500 0.000
## 6750 0.500
## 9000 0.625
## 11250 0.500
## 13500 0.500comparemethods.TPR$methodtype <- factor(comparemethods.TPR$method)
levels(comparemethods.TPR$methodtype)## [1] "cor_TPR_sal" "cor_TPR_temp" "LEA3.2_lfmm2_TPR_sal"
## [4] "LEA3.2_lfmm2_TPR_temp" "RDA_TPR" "RDA_TPR_corr"comparemethods.TPR$methodtype <- revalue(comparemethods.TPR$methodtype,
c("cor_TPR_sal"="Cor(p, Env2)",
"cor_TPR_temp"= "Cor(p, Temp.)",
"LEA3.2_lfmm2_TPR_sal" = "LFMM - Env2\n(structure correction)",
"LEA3.2_lfmm2_TPR_temp" = "LFMM - Temp.\n(structure correction)",
"RDA_TPR" = "RDA" ,
"RDA_TPR_corr" = "pRDA\n(structure correction)" ))
comparemethods.TPR$methodtype <- factor(comparemethods.TPR$methodtype,
levels = c("Cor(p, Temp.)",
"LFMM - Temp.\n(structure correction)",
"Cor(p, Env2)",
"LFMM - Env2\n(structure correction)",
"RDA",
"pRDA\n(structure correction)"),
ordered=TRUE)
table(comparemethods.TPR$methodtype) ##
## Cor(p, Temp.) LFMM - Temp.\n(structure correction)
## 2250 2250
## Cor(p, Env2) LFMM - Env2\n(structure correction)
## 2250 2250
## RDA pRDA\n(structure correction)
## 2250 2250g3<- ggplot(comparemethods.TPR, aes(x=as.factor(methodtype), y=TPR)) + geom_boxplot(color="grey", fill=rep(c("darkblue", "cornflowerblue"),3)) + ggtheme + xlab("method") + ylab("TPR\n(worse <<--->> better)") + scale_fill_viridis(option="turbo", discrete=TRUE) + theme(legend.position = "none") + theme( axis.text.x=element_text(angle=60, vjust=1, hjust=1, size = 10)) + ggtitle("B")
g3## Warning: Removed 900 rows containing non-finite values (stat_boxplot).
#pdf(paste0(outputs,"AUC-PR_FDR_TPR.pdf"), width=5, height=9)
#grid.arrange(g+ theme( axis.text.x = element_blank(), axis.title.x=element_blank()),
# g2+ theme( axis.text.x = element_blank(), axis.title.x=element_blank()),
# g3,
# layout_matrix=matrix(c(1,1,1,2,2,2,3,3,3,3,3), nrow=11))
#dev.off()
pdf(paste0(outputs,"GEAs-AUC.pdf"), width=5, height=4)
g## Warning: Removed 900 rows containing non-finite values (stat_boxplot).dev.off()## svg
## 2LFMM - TPR vs STRUCTURE
- If overcorrected, then expect power to decrease as the correlation between the latent factor and env
### THIS IS THE GRAPH
# Fix colors for arch_level_sub,
# rename legend and make it taller,
# reorder the legend levels
# For the 2nd legend, there is no "1 trait sims"
k1p <- ggplot(final.df) + geom_point(aes(x=abs(cor_LFMMU1_temp),y=LEA3.2_lfmm2_TPR_temp, color=arch_level_sub, shape=arch_level_sub)) + ggtheme +
xlab("|Cor(LFMM U1, Deme Temperature)|") +
ylab("TPR (LFMM Temp. Model)") +
scale_fill_viridis(option="magma", discrete=TRUE) +
scale_color_viridis(option="magma", discrete=TRUE, begin=0, end=0.9) +
geom_smooth(aes(x=abs(cor_LFMMU1_temp),y=LEA3.2_lfmm2_TPR_temp, color=arch_level_sub), alpha=0.2, method="lm", size=0.5) + ggtitle("A) Temp model") +
ylim(0,1) +xlim(0,1) + facet_wrap(~arch_level) + theme(legend.position = "none")
k1p # was not easy to change the color scheme here + scale_fill_viridis(option="mako", discrete=TRUE)## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing missing values (geom_smooth).
k2p <- ggplot(final.df) +
geom_point(aes(x=abs(cor_LFMMU1_sal),y=LEA3.2_lfmm2_TPR_sal, color=arch_level_sub, shape=arch_level_sub)) + ggtheme +
xlab("|Cor(LFMM U1, Deme Env2)|") + ylab("TPR (LFMM Env2 Model)") +
geom_smooth(aes(x=abs(cor_LFMMU1_sal),y=LEA3.2_lfmm2_TPR_sal, color=arch_level_sub), alpha=0.2, method="lm", size=0.5) +
ylim(0,1) +xlim(0,1) + ggtitle("B) Env2 model") +
scale_fill_viridis(option="magma", discrete=TRUE) +
scale_color_viridis(option="magma", discrete=TRUE, begin=0, end=0.9) +
facet_wrap(~arch_level) + theme(legend.position = "bottom")
k2p## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 450 rows containing non-finite values (stat_smooth).## Warning: Removed 450 rows containing missing values (geom_point).## Warning: Removed 32 rows containing missing values (geom_smooth).
lm_plot <- summary(lm(c(final.df$LEA3.2_lfmm2_TPR_temp,final.df$LEA3.2_lfmm2_TPR_sal)~
c(abs(final.df$cor_LFMMU1_temp), abs(final.df$cor_LFMMU1_sal))
))
lm_plot##
## Call:
## lm(formula = c(final.df$LEA3.2_lfmm2_TPR_temp, final.df$LEA3.2_lfmm2_TPR_sal) ~
## c(abs(final.df$cor_LFMMU1_temp), abs(final.df$cor_LFMMU1_sal)))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2668 -0.2453 -0.1162 0.1416 0.8827
##
## Coefficients:
## Estimate
## (Intercept) 0.266811
## c(abs(final.df$cor_LFMMU1_temp), abs(final.df$cor_LFMMU1_sal)) -0.237447
## Std. Error
## (Intercept) 0.005533
## c(abs(final.df$cor_LFMMU1_temp), abs(final.df$cor_LFMMU1_sal)) 0.032261
## t value Pr(>|t|)
## (Intercept) 48.22 < 2e-16
## c(abs(final.df$cor_LFMMU1_temp), abs(final.df$cor_LFMMU1_sal)) -7.36 2.21e-13
##
## (Intercept) ***
## c(abs(final.df$cor_LFMMU1_temp), abs(final.df$cor_LFMMU1_sal)) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3058 on 4048 degrees of freedom
## (450 observations deleted due to missingness)
## Multiple R-squared: 0.01321, Adjusted R-squared: 0.01296
## F-statistic: 54.17 on 1 and 4048 DF, p-value: 2.212e-13(slope=lm_plot$coefficients[2,1])## [1] -0.2374471(int <- lm_plot$coefficients[1,1])## [1] 0.266811### Create a plot showing overall pattern
mydf <- data.frame(y=c(final.df$LEA3.2_lfmm2_TPR_temp,final.df$LEA3.2_lfmm2_TPR_sal) ,
x = c(abs(final.df$cor_LFMMU1_temp), abs(final.df$cor_LFMMU1_sal)))
k3p <- ggplot(mydf) + geom_point(aes(x,y), alpha=0.5, color="tomato1") +
ggtheme +
xlab("|Cor(LFMM U1, Deme Environment)|") +
ylab("TPR (LFMM)") +
geom_smooth(aes(x,y), method="lm", size=0.5, color="black") +
ggtitle("C") +
ylim(0,1) +xlim(0,1) + theme(legend.position = "none")
k3p## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 450 rows containing non-finite values (stat_smooth).## Warning: Removed 450 rows containing missing values (geom_point).
pdf(paste0(outputs,"LFMM-TPRvsStructure.pdf"), width=8, height=8)
grid.arrange(k1p,k2p, nrow=2)## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing missing values (geom_smooth).## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 450 rows containing non-finite values (stat_smooth).## Warning: Removed 450 rows containing missing values (geom_point).## Warning: Removed 32 rows containing missing values (geom_smooth).# 450 missing values expected here
dev.off()## svg
## 2pdf(paste0(outputs,"GEA_results.pdf"), width=5, height=9)
grid.arrange(
g2+ theme( axis.text.x = element_blank(), axis.title.x=element_blank()),
g3,
k3p,
layout_matrix=matrix(c(1,1,1,2,2,2,2,2,3,3,3,3), nrow=12)
)## Warning: Removed 1422 rows containing non-finite values (stat_boxplot).## Warning: Removed 900 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 450 rows containing non-finite values (stat_smooth).## Warning: Removed 450 rows containing missing values (geom_point).dev.off()## svg
## 2RDA checks
par(mfrow=c(2,2))
sum(is.na(final.df$RDA_RDAmutpred_cor_tempEffect))## [1] 0sum(is.na(final.df$RDA_RDAmutpred_cor_salEffect)) #450 expected## [1] 450sum(is.na(final.df$RDA_cor_RDA20000temppredict_tempPhen))## [1] 0sum(is.na(final.df$RDA_cor_RDA20000salpredict_salPhen)) #450 expected## [1] 450# Seeds with really bad performance (no structure correction)
final.df$seed[which(final.df$RDA_RDAmutpred_cor_tempEffect < 0)]## [1] 1231305final.df$seed[which(final.df$RDA_RDAmutpred_cor_salEffect < 0)]## [1] 1231259 1231713 1231725 1232658 1233108 1233333final.df$seed[which(final.df$RDA_cor_RDA20000temppredict_tempPhen < 0)]## integer(0)final.df$seed[which(final.df$RDA_cor_RDA20000salppredict_salPhen < 0)]## integer(0)Can redundancy analysis identify mutations with pleiotropic effects?
g1<- ggplot(final.df, aes(x=RDA_RDAmutpred_cor_tempEffect, fill=arch_level)) + geom_histogram(color="grey40", binwidth=0.05) + ggtheme + coord_cartesian(xlim = c(-1, 1)) + xlab("Cor(QTN effect on temp,\nRDA-predicted temp. effect)") + guides(fill=guide_legend(title="Genetic\nArchitecture")) + scale_fill_viridis(option="rocket", discrete=TRUE) + theme(legend.position = "none") + ggtitle("A) Temperature mutation effect")
g1 
g2 <- ggplot(final.df, aes(x=RDA_RDAmutpred_cor_salEffect, fill=arch_level)) + geom_histogram(color="grey40", binwidth=0.05) + ggtheme+ coord_cartesian(xlim = c(-1, 1)) + xlab("Cor(QTN effect on Env2,\nRDA-predicted Env2 effect)") + guides(fill=guide_legend(title="Genetic\nArchitecture")) + scale_fill_viridis(option="rocket", discrete=TRUE) + theme(legend.position="bottom") + ggtitle("B) Env2 mutation effect")
g2## Warning: Removed 450 rows containing non-finite values (stat_bin).
# removing 450 expected
grid.arrange(g1, g2,
layout_matrix = matrix(c(1,1,1,2,2,2,2), nrow=7))## Warning: Removed 450 rows containing non-finite values (stat_bin).
# They show the same pattern, so combine for analysis:
forhist <- gather(final.df, key=env, value=Accuracy, RDA_RDAmutpred_cor_tempEffect, RDA_RDAmutpred_cor_salEffect)
g3 <- ggplot(forhist, aes(x=Accuracy, fill=arch_level)) + geom_histogram(color="grey40", binwidth=0.05) + ggtheme + coord_cartesian(xlim = c(-1, 1)) + xlab("Accuracy\nCor(evolved effect size,\n predicted effect size from RDA)") + guides(fill=guide_legend(title="Genetic\nArchitecture")) + scale_fill_viridis(option="rocket", discrete=TRUE) + ggtitle("D) Accuracy of RDA prediction \n (QTN effect size, no structure correction)")
g3## Warning: Removed 450 rows containing non-finite values (stat_bin).
# 450 missing values expectedSame analysis as above, but with structure correction
g1sc<- ggplot(final.df, aes(x=RDA_RDAmutpred_cor_tempEffect_structcorr, fill=arch_level)) + geom_histogram(color="grey40", binwidth=0.05) + ggtheme + coord_cartesian(xlim = c(-1, 1)) + xlab("Cor(QTN effect on temp,\nRDA-predicted temp. effect)") + guides(fill=guide_legend(title="Genetic\nArchitecture")) + scale_fill_viridis(option="rocket", discrete=TRUE) + theme(legend.position = "none") + ggtitle("A) Temperature mutation effect with structure correction")
g1sc
g2sc <- ggplot(final.df, aes(x=RDA_RDAmutpred_cor_salEffect_structcorr, fill=arch_level)) + geom_histogram(color="grey40", binwidth=0.05) + ggtheme + coord_cartesian(xlim = c(-1, 1)) + xlab("Cor(QTN effect on Env2,\nRDA-predicted Env2 effect)") + guides(fill=guide_legend(title="Genetic\nArchitecture")) + scale_fill_viridis(option="rocket", discrete=TRUE) + theme(legend.position="bottom") + ggtitle("B) Env2 mutation effect with structure correction")
g2sc## Warning: Removed 450 rows containing non-finite values (stat_bin).
grid.arrange(g1sc, g2sc,
layout_matrix = matrix(c(1,1,1,2,2,2,2), nrow=7))## Warning: Removed 450 rows containing non-finite values (stat_bin).
Another way to view the structure correction - boxplot
library(plyr)
### Temp
#### Check for missing data
sum(is.na(final.df$RDA_RDAmutpred_cor_tempEffect))## [1] 0 sum(is.na(final.df$RDA_RDAmutpred_cor_tempEffect_structcorr))## [1] 0 #### organize data
RDA_mutpredict <- gather(final.df, key=structcorr, value=Accuracy, RDA_RDAmutpred_cor_tempEffect, RDA_RDAmutpred_cor_tempEffect_structcorr)
RDA_mutpredict$demog_level <- factor(RDA_mutpredict$demog_level)
RDA_mutpredict$structcorr <- factor(RDA_mutpredict$structcorr)
RDA_mutpredict$structcorr <- revalue(RDA_mutpredict$structcorr,c( "RDA_RDAmutpred_cor_tempEffect" = "No (RDA)", "RDA_RDAmutpred_cor_tempEffect_structcorr" = "Yes (pRDA)"))
levels(RDA_mutpredict$structcorr)## [1] "No (RDA)" "Yes (pRDA)" # check again for missing data
sum(is.na(RDA_mutpredict$Accuracy))## [1] 0 n <- ggplot(RDA_mutpredict, aes(y=Accuracy,x=demog_level, fill=structcorr)) + geom_boxplot(color="grey40") + ggtheme + coord_cartesian(ylim = c(-1, 1)) + ylab("Accuracy of RDA prediction\n(worse <<----->> better)") + ggtitle("C) RDA prediction accuracy: QTN effect size on Temp.") + guides(fill=guide_legend(title="Structure\nCorrection?")) +
theme(axis.text.x=element_blank()) + xlab("")
# Cor(Mutation effect on Temp.,\nRDA-predicted Temp. effect)
n
# 450 rows
### Sal
RDA_mutpredict_sal <- gather(final.df, key=structcorr, value=Accuracy, RDA_RDAmutpred_cor_salEffect, RDA_RDAmutpred_cor_salEffect_structcorr)
RDA_mutpredict_sal$demog_level <- factor(RDA_mutpredict_sal$demog_level)
RDA_mutpredict_sal$structcorr <- factor(RDA_mutpredict_sal$structcorr)
RDA_mutpredict_sal$structcorr <- revalue(RDA_mutpredict_sal$structcorr,c( "RDA_RDAmutpred_cor_salEffect" = "No (RDA)", "RDA_RDAmutpred_cor_salEffect_structcorr" = "Yes (pRDA)"))
levels(RDA_mutpredict$structcorr)## [1] "No (RDA)" "Yes (pRDA)" # check for NA values
sum(is.na(RDA_mutpredict_sal$Accuracy))## [1] 900 # 900 expected (450 from each set combined to one data frame)
o <- ggplot(RDA_mutpredict_sal, aes(y=Accuracy,x=demog_level, fill=structcorr)) + geom_boxplot(color="grey40") + ggtheme + coord_cartesian(ylim = c(-1, 1)) + ylab("Accuracy of RDA prediction \n(worse <<----->> better)") + ggtitle("D) RDA prediction accuracy: QTN effect size on Env2") + guides(fill=guide_legend(title="Structure\nCorrection?")) + xlab("Landscape")
# "Cor(Mutation effect on Env2,\nRDA-predicted Env2 effect)"
o## Warning: Removed 900 rows containing non-finite values (stat_boxplot).
pdf(paste0(outputs,"RDA-Predict-Muts.pdf"), width=5.5, height=7)
grid.arrange(g3, n,o)## Warning: Removed 450 rows containing non-finite values (stat_bin).
## Warning: Removed 900 rows containing non-finite values (stat_boxplot).dev.off()## svg
## 2pdf(paste0(outputs,"RDA-Predict-Muts-nohist.pdf"), width=4, height=5.5)
grid.arrange(n,o)## Warning: Removed 900 rows containing non-finite values (stat_boxplot).dev.off()## svg
## 2What drives the population correction effect?
############
## Temp
############
## What drives the change in performance with structure correction?
final.df$change_temp_mut <- final.df$RDA_RDAmutpred_cor_tempEffect_structcorr - final.df$RDA_RDAmutpred_cor_tempEffect
summary(aov(final.df$change_temp_mut~final.df$cor_PC1_temp + final.df$cor_PC2_temp +
final.df$demog_level + final.df$demog_level_sub + final.df$arch_level))## Df Sum Sq Mean Sq F value Pr(>F)
## final.df$cor_PC1_temp 1 0.00 0.0002 0.005 0.942064
## final.df$cor_PC2_temp 1 0.43 0.4332 11.385 0.000753 ***
## final.df$demog_level 2 2.39 1.1972 31.465 3.34e-14 ***
## final.df$demog_level_sub 4 8.81 2.2021 57.878 < 2e-16 ***
## final.df$arch_level 2 3.20 1.5995 42.040 < 2e-16 ***
## Residuals 2239 85.19 0.0380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1p1 <- ggplot(final.df) +
geom_point(aes(x=abs(cor_PC1_temp), y=change_temp_mut, color=demog_level, shape=demog_level_sub), alpha=0.5) + ggtheme +
ylab("Change in accuracy\nafter structure correction\n(worse <<------->> better)") + xlab("|Cor(Structure as PC1, Temp)|") +
guides(color=guide_legend(title="Landscape"), shape=guide_legend("Demography")) + ggtitle("A) Mutation effect on Temperature") + coord_cartesian(ylim = c(-1, 1)) +
geom_smooth(aes(x=abs(cor_PC1_temp),y=change_temp_mut), color="tomato", method="loess", span=1) + scale_fill_discrete(guide="none") + geom_hline(yintercept=0, color="black")
p1## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'
#####################
## Env 2 trait
#####################
## What drives the change in performance with structure correction?
final.df$change_sal_mut <- final.df$RDA_RDAmutpred_cor_salEffect_structcorr - final.df$RDA_RDAmutpred_cor_salEffect
summary(aov(final.df$change_sal_mut~final.df$cor_PC1_sal+ final.df$cor_PC2_sal +
final.df$demog_level + final.df$demog_level_sub + final.df$arch_level))## Df Sum Sq Mean Sq F value Pr(>F)
## final.df$cor_PC1_sal 1 3.80 3.802 173.383 < 2e-16 ***
## final.df$cor_PC2_sal 1 0.00 0.002 0.111 0.739
## final.df$demog_level 2 36.25 18.126 826.617 < 2e-16 ***
## final.df$demog_level_sub 4 4.43 1.107 50.473 < 2e-16 ***
## final.df$arch_level 2 1.16 0.582 26.540 4.38e-12 ***
## Residuals 1789 39.23 0.022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 450 observations deleted due to missingnessp2 <- ggplot(final.df) + geom_point(aes(x=abs(cor_PC1_sal), y=change_sal_mut, color=demog_level, shape=demog_level_sub), alpha=0.5) + ggtheme + ylab("Change in accurcay\nafter structure correction\n(worse <<------->> better)") + xlab("|Cor(Structure as PC1, Env2)|") + guides(color=guide_legend(title="Landscape"), shape=guide_legend("Demography")) + ggtitle("B) Mutation effect on Env2") + coord_cartesian(ylim = c(-1, 1)) +
geom_smooth(aes(x=abs(cor_PC1_sal),y=change_sal_mut), method="loess", span=1, color="tomato") + scale_fill_discrete(guide="none") + geom_hline(yintercept=0, color="black")
p2## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 450 rows containing non-finite values (stat_smooth).## Warning: Removed 450 rows containing missing values (geom_point).
pdf(paste0(outputs,"RDA-Predict-Muts-CovariateStructure.pdf"), width=6, height=7)
grid.arrange(p1, p2)## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 450 rows containing non-finite values (stat_smooth).
## Warning: Removed 450 rows containing missing values (geom_point).dev.off()## svg
## 2Under what conditions can redundancy analysis predict an individual’s optimal environment, without knowledge of the genetic architecture of adaptation?
Visualize the effect of genetic architecture on phenotype prediction
forhist <- gather(final.df, key=env, value=Accuracy, RDA_cor_RDA20000temppredict_tempPhen, RDA_cor_RDA20000salpredict_salPhen) %>% filter(num_neut_postfilter>20000)
g4 <- ggplot(forhist, aes(x=Accuracy, fill=arch_level)) + geom_histogram(color="grey40", binwidth=0.05) + ggtheme + coord_cartesian(xlim = c(-1, 1)) + xlab("Accuracy\nCor(evolved phenotype,\n predicted phenotype from RDA)") + guides(fill=guide_legend(title="Genetic\nArchitecture")) + scale_fill_viridis(option="rocket", discrete=TRUE) + ggtitle("C) Accuracy of RDA prediction \n (individual trait value,\n no structure correction)")
g4## Warning: Removed 360 rows containing non-finite values (stat_bin).
Accuracy of RDA prediction maxed out by cor (phenotype, Env)
#Scatterplot
levels(final.df$demog_level_sub)## [1] "N equal\nm constant" "N equal\nm breaks"
## [3] "N latitude cline\nm constant" "N central cline\nm constant"
## [5] "N variable\nm variable"forscatter <- filter(final.df, num_neut_postfilter>20000) %>% arrange(desc(demog_level_sub))
n1 <- ggplot(forscatter) + geom_point(aes(x=subsamp_corr_phen_temp,
y=RDA_cor_RDA20000temppredict_tempPhen, bg=demog_level,
shape=demog_level), color="grey",alpha=0.7) + ylim(0,1) + xlim(0,1) +
ggtheme + geom_abline(slope=1, color="grey") +
scale_shape_manual(values = c(21, 22, 23), name="Landscape") + guides(bg = "none") +
xlab("Evolved phenotypic cline\nCor(Temp. phenotype, Deme Temp.)") +
ylab("Prediction accuracy\nCor(RDA prediction,\nTemp. phenotype)") + ggtitle("A) Accuracy of RDA prediction\n (Temp. trait, no structure correction)")
n1 
n2<- ggplot(forscatter) + geom_point(aes(x=subsamp_corr_phen_sal,
y=RDA_cor_RDA20000salpredict_salPhen, bg=demog_level,
shape=demog_level), alpha=0.7, color="grey") + ylim(0,1) + xlim(0,1) +
ggtheme + geom_abline(slope=1, color="grey") +
scale_shape_manual(values = c(21, 22, 23), name="Landscape") + guides(bg = "none") +
xlab("Evolved phenotypic cline\nCor(Env2 phenotype, Deme Env2)") +
ylab("Prediction accuracy\nCor(RDA prediction,\nEnv2 phenotype)") +
ggtitle("B) Accuracy of RDA prediction\n (Env2 trait, no structure correction)")
n2 ## Warning: Removed 360 rows containing missing values (geom_point).
# missing values expected due to filtering
pdf(paste0(outputs,"RDA-Demog-scatter.pdf"), width=6, height=10)
grid.arrange(g4, n1, n2, ncol=1)## Warning: Removed 360 rows containing non-finite values (stat_bin).
## Warning: Removed 360 rows containing missing values (geom_point).dev.off()## svg
## 2pdf(paste0(outputs,"RDA-Demog-scatter-noHist.pdf"), width=4.5, height=7)
grid.arrange(n1, n2)## Warning: Removed 360 rows containing missing values (geom_point).dev.off()## svg
## 2pdf(paste0(outputs,"RDA-Hists-PopMut.pdf"), width=5, height=7)
grid.arrange(g4, g3)## Warning: Removed 360 rows containing non-finite values (stat_bin).## Warning: Removed 450 rows containing non-finite values (stat_bin).dev.off()## svg
## 2Effect of structure correction
# Reorganize data
RDA_indpredict <- gather(final.df, key=structcorr, value=Accuracy, RDA_cor_RDA20000temppredict_tempPhen, RDA_cor_RDA20000temppredict_tempPhen_structcorr)
RDA_indpredict$demog_level <- factor(RDA_indpredict$demog_level)
RDA_indpredict$structcorr <- factor(RDA_indpredict$structcorr)
RDA_indpredict$structcorr <- revalue(RDA_indpredict$structcorr,c( "RDA_cor_RDA20000temppredict_tempPhen" = "No (RDA)", "RDA_cor_RDA20000temppredict_tempPhen_structcorr" = "Yes (pRDA)"))
levels(RDA_indpredict$structcorr)## [1] "No (RDA)" "Yes (pRDA)"n <- ggplot(RDA_indpredict, aes(y=Accuracy,x=demog_level, fill=structcorr)) + geom_boxplot(color="grey40") + ggtheme + ylim(-1,1) + ylab("Accuracy\nCor(evolved Temp. phenotype,\n predicted Temp. phenotype from RDA)") + ggtitle("A) Accuracy of RDA prediction\n (Temp. phenotype)") + guides(fill=guide_legend(title="Structure\nCorrection?")) + xlab("Landscape")
n
## Same as above for Env2 trait
RDA_indpredict_sal <- gather(final.df, key=structcorr, value=Accuracy, RDA_cor_RDA20000salpredict_salPhen, RDA_cor_RDA20000salpredict_salPhen_structcorr)
RDA_indpredict_sal$demog_level <- factor(RDA_indpredict_sal$demog_level)
RDA_indpredict_sal$structcorr <- factor(RDA_indpredict_sal$structcorr)
RDA_indpredict_sal$structcorr <- revalue(RDA_indpredict_sal$structcorr,c( "RDA_cor_RDA20000salpredict_salPhen" = "No (RDA)", "RDA_cor_RDA20000salpredict_salPhen_structcorr" = "Yes (pRDA)"))
levels(RDA_indpredict_sal$structcorr)## [1] "No (RDA)" "Yes (pRDA)"o <- ggplot(RDA_indpredict_sal, aes(y=Accuracy,x=demog_level, fill=structcorr)) + geom_boxplot(color="grey40") + ggtheme + ylim(-1,1) + ylab("Accuracy\nCor(evolved Env2. phenotype,\n predicted Env2. phenotype from RDA)") + ggtitle("B) Accuracy of RDA prediction\n (Env2. phenotype)") + guides(fill=guide_legend(title="Structure\nCorrection?")) + xlab("Landscape")
o## Warning: Removed 900 rows containing non-finite values (stat_boxplot).
# 900 missing values expected
pdf(paste0(outputs,"RDA-Predict-Phen-struct.pdf"), width=5, height=7)
grid.arrange(n, o)## Warning: Removed 900 rows containing non-finite values (stat_boxplot).dev.off()## svg
## 2Understand how correlation with PC affects structure correction
## Temp
## What drives the change in performance with structure correction?
final.df$change_temp <- final.df$RDA_cor_RDA20000temppredict_tempPhen_structcorr - final.df$RDA_cor_RDA20000temppredict_tempPhen
summary(aov(final.df$change_temp~final.df$cor_PC1_temp + final.df$cor_PC2_temp +
final.df$demog_level + final.df$demog_level_sub + final.df$arch_level))## Df Sum Sq Mean Sq F value Pr(>F)
## final.df$cor_PC1_temp 1 4.453 4.453 413.204 < 2e-16 ***
## final.df$cor_PC2_temp 1 0.195 0.195 18.070 2.22e-05 ***
## final.df$demog_level 2 6.013 3.007 278.989 < 2e-16 ***
## final.df$demog_level_sub 4 9.961 2.490 231.062 < 2e-16 ***
## final.df$arch_level 2 0.032 0.016 1.489 0.226
## Residuals 2239 24.129 0.011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1p1 <- ggplot(final.df) +
geom_point(aes(x=abs(cor_PC1_temp), y=change_temp, color=demog_level, shape=demog_level_sub), alpha=0.5) + ggtheme +
ylab("Change in accuracy\nafter structure correction\n(worse <<------->> better)") + xlab("|Cor(Structure as PC1, Temp)|") +
guides(color=guide_legend(title="Landscape"), shape=guide_legend("Demography")) + ggtitle("A) Temperature Phenotype") + ylim(-1,1) +
geom_smooth(aes(x=abs(cor_PC1_temp),y=change_temp, color=demog_level, fill=demog_level), alpha=0.2, method="loess", span=1) + scale_fill_discrete(guide="none")
p1## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
#####################
## Env 2 trait
#####################
## What drives the change in performance with structure correction?
final.df$change_sal <- final.df$RDA_cor_RDA20000salpredict_salPhen_structcorr - final.df$RDA_cor_RDA20000salpredict_salPhen
summary(aov(final.df$change_sal~final.df$cor_PC1_sal+ final.df$cor_PC2_sal +
final.df$demog_level + final.df$demog_level_sub + final.df$arch_level))## Df Sum Sq Mean Sq F value Pr(>F)
## final.df$cor_PC1_sal 1 20.55 20.55 2509.144 < 2e-16 ***
## final.df$cor_PC2_sal 1 0.10 0.10 11.756 0.00062 ***
## final.df$demog_level 2 133.70 66.85 8160.706 < 2e-16 ***
## final.df$demog_level_sub 4 6.21 1.55 189.427 < 2e-16 ***
## final.df$arch_level 2 0.01 0.00 0.562 0.57034
## Residuals 1789 14.66 0.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 450 observations deleted due to missingnessp2 <- ggplot(final.df) + geom_point(aes(x=abs(cor_PC1_sal), y=change_sal, color=demog_level, shape=demog_level_sub), alpha=0.5) + ggtheme + ylab("Change in accuracy\nafter structure correction\n(worse <<------->> better)") + xlab("|Cor(Structure as PC1, Env2)|") + guides(color=guide_legend(title="Landscape"), shape=guide_legend("Demography")) + ggtitle("B) Env2 Phenotype") + ylim(-1,1) +
geom_smooth(aes(x=abs(cor_PC1_sal),y=change_sal, color=demog_level, fill=demog_level), alpha=0.2, method="loess", span=1) + scale_fill_discrete(guide="none")
p2## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 450 rows containing non-finite values (stat_smooth).## Warning: Removed 450 rows containing missing values (geom_point).
pdf(paste0(outputs,"RDA-Predict-Traits-CovariateStructure.pdf"), width=6, height=7)
grid.arrange(p1, p2)## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Using shapes for an ordinal variable is not advised## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 450 rows containing non-finite values (stat_smooth).## Warning: Removed 450 rows containing missing values (geom_point).dev.off()## svg
## 2#hist(final.df$cor_RDA20000_RDloadings_tempPhen, xlim=c(0,1), breaks=seq(0,1,0.05))
#hist(final.df$cor_RDA20000_RDloadings_salPhen, xlim=c(0,1), breaks=seq(0,1,0.05))Statistical models
length(levels(final.df$arch))## [1] 15# Stat models for temperature effect prediction
tm_me <- summary(aov(RDA_RDAmutpred_cor_tempEffect~ subsamp_corr_phen_temp + demog_level + demog_level_sub + arch + demog_level*demog_level_sub*arch, data=final.df))
tm_me## Df Sum Sq Mean Sq F value Pr(>F)
## subsamp_corr_phen_temp 1 2.42 2.423 228.140 < 2e-16 ***
## demog_level 2 5.79 2.893 272.483 < 2e-16 ***
## demog_level_sub 4 0.48 0.119 11.199 5.48e-09 ***
## arch 14 58.93 4.209 396.406 < 2e-16 ***
## demog_level:demog_level_sub 8 0.14 0.017 1.631 0.1109
## demog_level:arch 28 1.29 0.046 4.343 3.68e-13 ***
## demog_level_sub:arch 56 1.59 0.028 2.667 5.82e-10 ***
## demog_level:demog_level_sub:arch 112 1.47 0.013 1.236 0.0515 .
## Residuals 2024 21.49 0.011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 sm_me <- summary(aov(RDA_RDAmutpred_cor_salEffect~ subsamp_corr_phen_temp + demog_level + demog_level_sub + arch + demog_level*demog_level_sub*arch, data=final.df))
sm_me## Df Sum Sq Mean Sq F value Pr(>F)
## subsamp_corr_phen_temp 1 0.867 0.867 60.947 1.05e-14 ***
## demog_level 2 7.493 3.746 263.414 < 2e-16 ***
## demog_level_sub 4 3.031 0.758 53.283 < 2e-16 ***
## arch 11 23.549 2.141 150.526 < 2e-16 ***
## demog_level:demog_level_sub 8 1.386 0.173 12.177 < 2e-16 ***
## demog_level:arch 22 6.739 0.306 21.536 < 2e-16 ***
## demog_level_sub:arch 44 1.603 0.036 2.561 1.28e-07 ***
## demog_level:demog_level_sub:arch 88 1.769 0.020 1.413 0.00822 **
## Residuals 1619 23.026 0.014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 450 observations deleted due to missingness# STat models for phenotype prediction
tm_pp <- summary(aov(RDA_cor_RDA20000temppredict_tempPhen~ subsamp_corr_phen_temp + demog_level + demog_level_sub + arch + demog_level*demog_level_sub*arch, data=final.df))
tm_pp## Df Sum Sq Mean Sq F value Pr(>F)
## subsamp_corr_phen_temp 1 14.407 14.407 59565.024 < 2e-16 ***
## demog_level 2 0.009 0.005 19.303 4.96e-09 ***
## demog_level_sub 4 0.384 0.096 396.873 < 2e-16 ***
## arch 14 0.027 0.002 8.071 < 2e-16 ***
## demog_level:demog_level_sub 8 0.039 0.005 20.105 < 2e-16 ***
## demog_level:arch 28 0.016 0.001 2.363 7.67e-05 ***
## demog_level_sub:arch 56 0.060 0.001 4.444 < 2e-16 ***
## demog_level:demog_level_sub:arch 112 0.034 0.000 1.248 0.0438 *
## Residuals 2024 0.490 0.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sm_pp <- summary(aov(RDA_cor_RDA20000salpredict_salPhen~ subsamp_corr_phen_sal + demog_level + demog_level_sub + arch + demog_level*demog_level_sub*arch, data=final.df))
sm_pp## Df Sum Sq Mean Sq F value Pr(>F)
## subsamp_corr_phen_sal 1 8.583 8.583 8167.415 < 2e-16 ***
## demog_level 2 0.499 0.249 237.396 < 2e-16 ***
## demog_level_sub 4 3.550 0.887 844.434 < 2e-16 ***
## arch 11 0.076 0.007 6.591 7.49e-11 ***
## demog_level:demog_level_sub 8 1.187 0.148 141.149 < 2e-16 ***
## demog_level:arch 22 0.071 0.003 3.057 2.49e-06 ***
## demog_level_sub:arch 44 0.085 0.002 1.830 0.000815 ***
## demog_level:demog_level_sub:arch 88 0.176 0.002 1.900 1.93e-06 ***
## Residuals 1619 1.701 0.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 450 observations deleted due to missingness # Calculate proportion of variance explained in SS
z<- data.frame(
PropVarTempMutPred = round(tm_me[[1]][,2]/sum(tm_me[[1]][,2]), 2),
PropVarTempPhenPred = round(tm_pp[[1]][,2]/sum(tm_pp[[1]][,2]), 2),
PropVarEnv2MutPred = round(sm_me[[1]][,2]/sum(sm_me[[1]][,2]), 2),
PropVarEnv2PhenPred = round(sm_pp[[1]][,2]/sum(sm_pp[[1]][,2]), 2))
# note the the 1st row is specific for each trait, I just took a shortcut for labeling the rowname
rownames(z) <- rownames(tm_me[[1]])
z## PropVarTempMutPred PropVarTempPhenPred
## subsamp_corr_phen_temp 0.03 0.93
## demog_level 0.06 0.00
## demog_level_sub 0.01 0.02
## arch 0.63 0.00
## demog_level:demog_level_sub 0.00 0.00
## demog_level:arch 0.01 0.00
## demog_level_sub:arch 0.02 0.00
## demog_level:demog_level_sub:arch 0.02 0.00
## Residuals 0.23 0.03
## PropVarEnv2MutPred PropVarEnv2PhenPred
## subsamp_corr_phen_temp 0.01 0.54
## demog_level 0.11 0.03
## demog_level_sub 0.04 0.22
## arch 0.34 0.00
## demog_level:demog_level_sub 0.02 0.07
## demog_level:arch 0.10 0.00
## demog_level_sub:arch 0.02 0.01
## demog_level:demog_level_sub:arch 0.03 0.01
## Residuals 0.33 0.11The models clarify how the architecture determines the proportion of clines - architecture also largely determines how accurate the mutation prediction is - yet the RDA phenotype prediction is not sensitive to the architecture and it is driven more by how correlated the phenotype is with the environment.
Interesting sims to look at
This code just copies some of the figures into a new folder that I can download from the cluster.
RDA predictions as a function of the number of loci and structure correction
rdapred <- read.table("summary_20220428_20220726_RDApredictions.txt", header=TRUE)
head(rdapred)## i nloci Random_cor_RDAtemppredict_tempphen Random_cor_RDAsalpredict_salphen
## 1 1 10 0.4739758 NA
## 2 2 50 0.7151539 NA
## 3 3 100 0.7974522 NA
## 4 4 500 0.8329278 NA
## 5 5 5000 0.8513302 NA
## 6 6 20000 0.8530879 NA
## Random_cor_RDAtemppredict_tempphen_structcorr
## 1 0.042232275
## 2 0.019313333
## 3 -0.001635637
## 4 -0.039277317
## 5 -0.033347381
## 6 -0.035417453
## Random_cor_RDAsalpredict_salphen_structcorr seed
## 1 NA 1231094
## 2 NA 1231094
## 3 NA 1231094
## 4 NA 1231094
## 5 NA 1231094
## 6 NA 1231094rdapred_df <- merge(rdapred, final.df, by="seed", all.x=TRUE)
head(rdapred_df)## seed i nloci Random_cor_RDAtemppredict_tempphen
## 1 1231094 1 10 0.4739758
## 2 1231094 2 50 0.7151539
## 3 1231094 3 100 0.7974522
## 4 1231094 4 500 0.8329278
## 5 1231094 5 5000 0.8513302
## 6 1231094 6 20000 0.8530879
## Random_cor_RDAsalpredict_salphen
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## Random_cor_RDAtemppredict_tempphen_structcorr
## 1 0.042232275
## 2 0.019313333
## 3 -0.001635637
## 4 -0.039277317
## 5 -0.033347381
## 6 -0.035417453
## Random_cor_RDAsalpredict_salphen_structcorr
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## level reps
## 1 highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 1
## 2 highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 1
## 3 highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 1
## 4 highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 1
## 5 highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 1
## 6 highly-polygenic_1-trait__Est-Clines_N-cline-center-to-edge_m-constant 1
## arch demog_name
## 1 highly-polygenic_1-trait Est-Clines_N-cline-center-to-edge_m-constant
## 2 highly-polygenic_1-trait Est-Clines_N-cline-center-to-edge_m-constant
## 3 highly-polygenic_1-trait Est-Clines_N-cline-center-to-edge_m-constant
## 4 highly-polygenic_1-trait Est-Clines_N-cline-center-to-edge_m-constant
## 5 highly-polygenic_1-trait Est-Clines_N-cline-center-to-edge_m-constant
## 6 highly-polygenic_1-trait Est-Clines_N-cline-center-to-edge_m-constant
## demog_level_sub demog_level MIG_x MIG_y xcline ycline demog
## 1 N central cline\nm constant Est-Clines 0.49 0.07 linear linear Estuary
## 2 N central cline\nm constant Est-Clines 0.49 0.07 linear linear Estuary
## 3 N central cline\nm constant Est-Clines 0.49 0.07 linear linear Estuary
## 4 N central cline\nm constant Est-Clines 0.49 0.07 linear linear Estuary
## 5 N central cline\nm constant Est-Clines 0.49 0.07 linear linear Estuary
## 6 N central cline\nm constant Est-Clines 0.49 0.07 linear linear Estuary
## METAPOP_SIDE_x METAPOP_SIDE_y Nequal isVariableM MIG_breaks arch_level_sub
## 1 10 10 4 0 0 1 trait
## 2 10 10 4 0 0 1 trait
## 3 10 10 4 0 0 1 trait
## 4 10 10 4 0 0 1 trait
## 5 10 10 4 0 0 1 trait
## 6 10 10 4 0 0 1 trait
## arch_level MU_base MU_QTL_proportion SIGMA_QTN_1 SIGMA_QTN_2 SIGMA_K_1
## 1 highly\npolygenic 1e-07 0.25 0.002 0.002 0.5
## 2 highly\npolygenic 1e-07 0.25 0.002 0.002 0.5
## 3 highly\npolygenic 1e-07 0.25 0.002 0.002 0.5
## 4 highly\npolygenic 1e-07 0.25 0.002 0.002 0.5
## 5 highly\npolygenic 1e-07 0.25 0.002 0.002 0.5
## 6 highly\npolygenic 1e-07 0.25 0.002 0.002 0.5
## SIGMA_K_2 N_traits ispleiotropy n_samp_tot n_samp_per_pop
## 1 0.5 1 No pleiotropy 1000 10
## 2 0.5 1 No pleiotropy 1000 10
## 3 0.5 1 No pleiotropy 1000 10
## 4 0.5 1 No pleiotropy 1000 10
## 5 0.5 1 No pleiotropy 1000 10
## 6 0.5 1 No pleiotropy 1000 10
## sd_fitness_among_inds sd_fitness_among_pops final_LA K Bonf_alpha
## 1 0.05272602 0.01977273 0.500854 2 1.918428e-06
## 2 0.05272602 0.01977273 0.500854 2 1.918428e-06
## 3 0.05272602 0.01977273 0.500854 2 1.918428e-06
## 4 0.05272602 0.01977273 0.500854 2 1.918428e-06
## 5 0.05272602 0.01977273 0.500854 2 1.918428e-06
## 6 0.05272602 0.01977273 0.500854 2 1.918428e-06
## numCausalLowMAFsample all_corr_phen_temp subsamp_corr_phen_temp
## 1 39 0.954579 0.8893387
## 2 39 0.954579 0.8893387
## 3 39 0.954579 0.8893387
## 4 39 0.954579 0.8893387
## 5 39 0.954579 0.8893387
## 6 39 0.954579 0.8893387
## all_corr_phen_sal subsamp_corr_phen_sal num_causal_prefilter
## 1 NA NA 2628
## 2 NA NA 2628
## 3 NA NA 2628
## 4 NA NA 2628
## 5 NA NA 2628
## 6 NA NA 2628
## num_causal_postfilter num_non_causal num_neut_prefilter num_neut_postfilter
## 1 310 25753 26587 26587
## 2 310 25753 26587 26587
## 3 310 25753 26587 26587
## 4 310 25753 26587 26587
## 5 310 25753 26587 26587
## 6 310 25753 26587 26587
## num_neut_neutralgenome num_causal_temp num_causal_sal num_multiallelic
## 1 12867 310 0 0
## 2 12867 310 0 0
## 3 12867 310 0 0
## 4 12867 310 0 0
## 5 12867 310 0 0
## 6 12867 310 0 0
## meanFst va_temp_total va_sal_total Va_temp_sample Va_sal_sample nSNPs
## 1 0.1803949 0.01185008 0 0.01356519 0 26063
## 2 0.1803949 0.01185008 0 0.01356519 0 26063
## 3 0.1803949 0.01185008 0 0.01356519 0 26063
## 4 0.1803949 0.01185008 0 0.01356519 0 26063
## 5 0.1803949 0.01185008 0 0.01356519 0 26063
## 6 0.1803949 0.01185008 0 0.01356519 0 26063
## median_causal_temp_cor median_causal_sal_cor median_neut_temp_cor
## 1 0.3580437 NA 0.3820531
## 2 0.3580437 NA 0.3820531
## 3 0.3580437 NA 0.3820531
## 4 0.3580437 NA 0.3820531
## 5 0.3580437 NA 0.3820531
## 6 0.3580437 NA 0.3820531
## median_neut_sal_cor cor_VA_temp_prop cor_VA_sal_prop cor_TPR_temp cor_TPR_sal
## 1 0.04816897 0.8451605 0 0.4612903 NA
## 2 0.04816897 0.8451605 0 0.4612903 NA
## 3 0.04816897 0.8451605 0 0.4612903 NA
## 4 0.04816897 0.8451605 0 0.4612903 NA
## 5 0.04816897 0.8451605 0 0.4612903 NA
## 6 0.04816897 0.8451605 0 0.4612903 NA
## cor_FDR_allSNPs_temp cor_FDR_neutSNPs_temp cor_FDR_allSNPs_sal
## 1 0.9894449 0.9791241 NA
## 2 0.9894449 0.9791241 NA
## 3 0.9894449 0.9791241 NA
## 4 0.9894449 0.9791241 NA
## 5 0.9894449 0.9791241 NA
## 6 0.9894449 0.9791241 NA
## cor_FDR_neutSNPs_sal num_causal_sig_temp_corr num_causal_sig_sal_corr
## 1 NA 143 0
## 2 NA 143 0
## 3 NA 143 0
## 4 NA 143 0
## 5 NA 143 0
## 6 NA 143 0
## num_notCausal_sig_temp_corr num_notCausal_sig_sal_corr num_neut_sig_temp_corr
## 1 13405 0 6707
## 2 13405 0 6707
## 3 13405 0 6707
## 4 13405 0 6707
## 5 13405 0 6707
## 6 13405 0 6707
## num_neut_sig_sal_corr cor_AUCPR_temp_allSNPs cor_AUCPR_temp_neutSNPs
## 1 0 0.01064045 0.02148047
## 2 0 0.01064045 0.02148047
## 3 0 0.01064045 0.02148047
## 4 0 0.01064045 0.02148047
## 5 0 0.01064045 0.02148047
## 6 0 0.01064045 0.02148047
## cor_AUCPR_sal_allSNPs cor_AUCPR_sal_neutSNPs cor_af_temp_noutliers
## 1 NA NA 13548
## 2 NA NA 13548
## 3 NA NA 13548
## 4 NA NA 13548
## 5 NA NA 13548
## 6 NA NA 13548
## cor_af_sal_noutliers cor_FPR_temp_neutSNPs cor_FPR_sal_neutSNPs
## 1 0 0.5212559 0
## 2 0 0.5212559 0
## 3 0 0.5212559 0
## 4 0 0.5212559 0
## 5 0 0.5212559 0
## 6 0 0.5212559 0
## LEA3.2_lfmm2_Va_temp_prop LEA3.2_lfmm2_Va_sal_prop LEA3.2_lfmm2_TPR_temp
## 1 0.278265 NA 0.02258065
## 2 0.278265 NA 0.02258065
## 3 0.278265 NA 0.02258065
## 4 0.278265 NA 0.02258065
## 5 0.278265 NA 0.02258065
## 6 0.278265 NA 0.02258065
## LEA3.2_lfmm2_TPR_sal LEA3.2_lfmm2_FDR_allSNPs_temp
## 1 NA 0.978979
## 2 NA 0.978979
## 3 NA 0.978979
## 4 NA 0.978979
## 5 NA 0.978979
## 6 NA 0.978979
## LEA3.2_lfmm2_FDR_allSNPs_sal LEA3.2_lfmm2_FDR_neutSNPs_temp
## 1 NA 0.9263158
## 2 NA 0.9263158
## 3 NA 0.9263158
## 4 NA 0.9263158
## 5 NA 0.9263158
## 6 NA 0.9263158
## LEA3.2_lfmm2_FDR_neutSNPs_sal LEA3.2_lfmm2_AUCPR_temp_allSNPs
## 1 NA 0.01339495
## 2 NA 0.01339495
## 3 NA 0.01339495
## 4 NA 0.01339495
## 5 NA 0.01339495
## 6 NA 0.01339495
## LEA3.2_lfmm2_AUCPR_temp_neutSNPs LEA3.2_lfmm2_AUCPR_sal_allSNPs
## 1 0.02655237 NA
## 2 0.02655237 NA
## 3 0.02655237 NA
## 4 0.02655237 NA
## 5 0.02655237 NA
## 6 0.02655237 NA
## LEA3.2_lfmm2_AUCPR_sal_neutSNPs LEA3.2_lfmm2_mlog10P_tempenv_noutliers
## 1 NA 333
## 2 NA 333
## 3 NA 333
## 4 NA 333
## 5 NA 333
## 6 NA 333
## LEA3.2_lfmm2_mlog10P_salenv_noutliers LEA3.2_lfmm2_num_causal_sig_temp
## 1 0 7
## 2 0 7
## 3 0 7
## 4 0 7
## 5 0 7
## 6 0 7
## LEA3.2_lfmm2_num_neut_sig_temp LEA3.2_lfmm2_num_causal_sig_sal
## 1 88 0
## 2 88 0
## 3 88 0
## 4 88 0
## 5 88 0
## 6 88 0
## LEA3.2_lfmm2_num_neut_sig_sal LEA3.2_lfmm2_FPR_neutSNPs_temp
## 1 0 0.006839201
## 2 0 0.006839201
## 3 0 0.006839201
## 4 0 0.006839201
## 5 0 0.006839201
## 6 0 0.006839201
## LEA3.2_lfmm2_FPR_neutSNPs_sal RDA1_propvar RDA2_propvar RDA1_propvar_corr
## 1 0 0.985 0.015 0.765
## 2 0 0.985 0.015 0.765
## 3 0 0.985 0.015 0.765
## 4 0 0.985 0.015 0.765
## 5 0 0.985 0.015 0.765
## 6 0 0.985 0.015 0.765
## RDA2_propvar_corr RDA1_temp_cor RDA1_sal_cor RDA2_temp_cor RDA2_sal_cor
## 1 0.235 0.9999982 0.001912722 -0.001912722 0.9999982
## 2 0.235 0.9999982 0.001912722 -0.001912722 0.9999982
## 3 0.235 0.9999982 0.001912722 -0.001912722 0.9999982
## 4 0.235 0.9999982 0.001912722 -0.001912722 0.9999982
## 5 0.235 0.9999982 0.001912722 -0.001912722 0.9999982
## 6 0.235 0.9999982 0.001912722 -0.001912722 0.9999982
## RDA_Va_temp_prop RDA_Va_temp_prop_corr RDA_Va_sal_prop RDA_Va_sal_prop_corr
## 1 0.1248244 0 0 0
## 2 0.1248244 0 0 0
## 3 0.1248244 0 0 0
## 4 0.1248244 0 0 0
## 5 0.1248244 0 0 0
## 6 0.1248244 0 0 0
## RDA_TPR RDA_TPR_corr RDA_FDR_allSNPs RDA_FDR_allSNPs_corr
## 1 0.006451613 0 0.9918699 1
## 2 0.006451613 0 0.9918699 1
## 3 0.006451613 0 0.9918699 1
## 4 0.006451613 0 0.9918699 1
## 5 0.006451613 0 0.9918699 1
## 6 0.006451613 0 0.9918699 1
## num_RDA_sig_causal num_RDA_sig_neutral num_RDA_sig_causal_corr
## 1 2 118 0
## 2 2 118 0
## 3 2 118 0
## 4 2 118 0
## 5 2 118 0
## 6 2 118 0
## num_RDA_sig_neutral_corr RDA_FDR_neutSNPs RDA_FDR_neutSNPs_corr
## 1 200 0.9833333 1
## 2 200 0.9833333 1
## 3 200 0.9833333 1
## 4 200 0.9833333 1
## 5 200 0.9833333 1
## 6 200 0.9833333 1
## RDA_AUCPR_allSNPs RDA_AUCPR_neutSNPs RDA_AUCPR_neutSNPs_corr RDA_FPR_neutSNPs
## 1 0.008249728 0.01647883 0.01597697 0.009170747
## 2 0.008249728 0.01647883 0.01597697 0.009170747
## 3 0.008249728 0.01647883 0.01597697 0.009170747
## 4 0.008249728 0.01647883 0.01597697 0.009170747
## 5 0.008249728 0.01647883 0.01597697 0.009170747
## 6 0.008249728 0.01647883 0.01597697 0.009170747
## RDA_FPR_neutSNPs_corr RDA_RDAmutpred_cor_tempEffect
## 1 0.01554364 0.2566239
## 2 0.01554364 0.2566239
## 3 0.01554364 0.2566239
## 4 0.01554364 0.2566239
## 5 0.01554364 0.2566239
## 6 0.01554364 0.2566239
## RDA_RDAmutpred_cor_salEffect RDA_absRDAmutpred_cor_tempVa
## 1 NA -0.04251831
## 2 NA -0.04251831
## 3 NA -0.04251831
## 4 NA -0.04251831
## 5 NA -0.04251831
## 6 NA -0.04251831
## RDA_absRDAmutpred_cor_salVa RDA_RDAmutpred_cor_tempEffect_structcorr
## 1 NA 0.1711607
## 2 NA 0.1711607
## 3 NA 0.1711607
## 4 NA 0.1711607
## 5 NA 0.1711607
## 6 NA 0.1711607
## RDA_RDAmutpred_cor_salEffect_structcorr
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## RDA_absRDAmutpred_cor_tempVa_structcorr
## 1 0.002950593
## 2 0.002950593
## 3 0.002950593
## 4 0.002950593
## 5 0.002950593
## 6 0.002950593
## RDA_absRDAmutpred_cor_salVa_structcorr RDA_cor_RDA20000temppredict_tempPhen
## 1 NA 0.8530879
## 2 NA 0.8530879
## 3 NA 0.8530879
## 4 NA 0.8530879
## 5 NA 0.8530879
## 6 NA 0.8530879
## RDA_cor_RDA20000salpredict_salPhen
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## RDA_cor_RDA20000temppredict_tempPhen_structcorr
## 1 -0.03541745
## 2 -0.03541745
## 3 -0.03541745
## 4 -0.03541745
## 5 -0.03541745
## 6 -0.03541745
## RDA_cor_RDA20000salpredict_salPhen_structcorr cor_PC1_temp cor_PC1_sal
## 1 NA -0.9938219 -0.002423473
## 2 NA -0.9938219 -0.002423473
## 3 NA -0.9938219 -0.002423473
## 4 NA -0.9938219 -0.002423473
## 5 NA -0.9938219 -0.002423473
## 6 NA -0.9938219 -0.002423473
## cor_PC2_temp cor_PC2_sal cor_LFMMU1_temp cor_LFMMU1_sal cor_LFMMU2_temp
## 1 -0.02040717 -0.00168847 0.0702796 -0.001851458 -0.2374149
## 2 -0.02040717 -0.00168847 0.0702796 -0.001851458 -0.2374149
## 3 -0.02040717 -0.00168847 0.0702796 -0.001851458 -0.2374149
## 4 -0.02040717 -0.00168847 0.0702796 -0.001851458 -0.2374149
## 5 -0.02040717 -0.00168847 0.0702796 -0.001851458 -0.2374149
## 6 -0.02040717 -0.00168847 0.0702796 -0.001851458 -0.2374149
## cor_LFMMU2_sal cor_PC1_LFMMU1_temp cor_PC1_LFMMU1_sal cor_PC2_LFMMU1_temp
## 1 -0.002823011 -0.09231664 0.9995526 0.9926958
## 2 -0.002823011 -0.09231664 0.9995526 0.9926958
## 3 -0.002823011 -0.09231664 0.9995526 0.9926958
## 4 -0.002823011 -0.09231664 0.9995526 0.9926958
## 5 -0.002823011 -0.09231664 0.9995526 0.9926958
## 6 -0.002823011 -0.09231664 0.9995526 0.9926958
## cor_PC2_LFMMU1_sal gwas_TPR_sal gwas_TPR_temp gwas_FDR_sal_neutbase
## 1 0.001339894 NA 0.2290323 NA
## 2 0.001339894 NA 0.2290323 NA
## 3 0.001339894 NA 0.2290323 NA
## 4 0.001339894 NA 0.2290323 NA
## 5 0.001339894 NA 0.2290323 NA
## 6 0.001339894 NA 0.2290323 NA
## gwas_FDR_temp_neutbase clinalparadigm_sal_proptop5GWASclines
## 1 0.9667914 NA
## 2 0.9667914 NA
## 3 0.9667914 NA
## 4 0.9667914 NA
## 5 0.9667914 NA
## 6 0.9667914 NA
## clinalparadigm_temp_proptop5GWASclines clinalparadigm_sal_propsigGWASclines
## 1 0.6554106 NA
## 2 0.6554106 NA
## 3 0.6554106 NA
## 4 0.6554106 NA
## 5 0.6554106 NA
## 6 0.6554106 NA
## clinalparadigm_temp_propsigGWASclines N_traits2 change_temp_mut
## 1 0.6050302 1 Trait -0.08546318
## 2 0.6050302 1 Trait -0.08546318
## 3 0.6050302 1 Trait -0.08546318
## 4 0.6050302 1 Trait -0.08546318
## 5 0.6050302 1 Trait -0.08546318
## 6 0.6050302 1 Trait -0.08546318
## change_sal_mut change_temp change_sal
## 1 NA -0.8885054 NA
## 2 NA -0.8885054 NA
## 3 NA -0.8885054 NA
## 4 NA -0.8885054 NA
## 5 NA -0.8885054 NA
## 6 NA -0.8885054 NArdapred_df$SIGMA_K_2 <- as.factor(as.character(rdapred_df$SIGMA_K_2))
rdapred_df1 <- filter(rdapred_df, nloci %in% c(10,50,500,5000,20000))
r1<- ggplot(rdapred_df1 ) +
geom_line( aes(x=nloci, y=Random_cor_RDAtemppredict_tempphen, group=seed, color=SIGMA_K_2), alpha=0.1, linetype="dashed") + facet_wrap(~arch_level) + ggtheme + ylim(-1,1) + ylab("Accuracy of RDA prediction\nTemperature trait\n worse <<--->> better") + theme(legend.position = "none") + ggtitle("A) Temperature trait") + stat_summary(aes(x=nloci, y=Random_cor_RDAtemppredict_tempphen, color=SIGMA_K_2), size=2,geom="line", fun="mean") + xlab("Number of random loci from genome") + theme(axis.text.x = element_text(angle = 60, vjust=1, hjust=1))
r1
r2 <- ggplot(rdapred_df1) +
geom_line(aes(x=nloci, y=Random_cor_RDAtemppredict_tempphen_structcorr, group=seed, color=SIGMA_K_2), alpha=0.1, linetype="dashed") + facet_wrap(~arch_level) + ggtheme + ylim(-1,1)+ ylab("Accuracy of structure-\ncorrected RDA prediction\nTemperature trait\n worse <<--->> better") + ggtitle("B) Temperature trait with structure correction")+ guides(color=guide_legend(title="Strength of\nstabilizing\nselection")) + xlab("Number of random loci from genome") + theme(axis.text.x = element_text(angle = 60, vjust=1, hjust=1)) + stat_summary(aes(x=nloci, y=Random_cor_RDAtemppredict_tempphen_structcorr, color=SIGMA_K_2), size=2,geom="line", fun="mean") + xlab("Number of random loci from genome") + scale_color_hue(labels=c(expression(paste("Strong ", sigma[K]," = 0.5")),
expression(paste("Weak ", sigma[K]," = 4.0"))))
r2
r3 <- ggplot(rdapred_df1) +
geom_line(aes(x=nloci, y=Random_cor_RDAsalpredict_salphen, group=seed, color=as.factor(demog_level)), alpha=0.1, linetype="dashed") + facet_wrap(~arch_level) + ggtheme + ylim(-1,1) + ylab("Accuracy of RDA prediction\nEnv2 trait\n worse <<--->> better") + theme(legend.position = "none") + ggtitle("C) Env2 trait") + stat_summary(aes(x=nloci, y=Random_cor_RDAsalpredict_salphen, color=as.factor(demog_level)), size=2,geom="line", fun="mean") + xlab("Number of random loci from genome") + theme(axis.text.x = element_text(angle = 60, vjust=1, hjust=1))
r3## Warning: Removed 2160 rows containing non-finite values (stat_summary).## Warning: Removed 2160 row(s) containing missing values (geom_path).
r4 <- ggplot(rdapred_df1) +
geom_line(aes(x=nloci, y=Random_cor_RDAsalpredict_salphen_structcorr, group=seed, color=as.factor(demog_level)), alpha=0.1, linetype="dashed") + facet_wrap(~arch_level) + ggtheme + ylim(-1,1) + ylab("Accuracy of structure-\ncorrected RDA prediction\nEnv2 trait\n worse <<--->> better") + ggtitle("D) Env2 trait with structure correction") + guides(color=guide_legend(title="Landscape")) + stat_summary(aes(x=nloci, y=Random_cor_RDAsalpredict_salphen_structcorr, color=as.factor(demog_level)), size=2,geom="line", fun="mean") + xlab("Number of random loci from genome") + theme(axis.text.x = element_text(angle = 60, vjust=1, hjust=1))
r4## Warning: Removed 2160 rows containing non-finite values (stat_summary).
## Warning: Removed 2160 row(s) containing missing values (geom_path).
pdf(paste0(outputs,"RDApredictionFunctionOfnLoci.pdf"), width=12, height=8)
grid.arrange(r1,r2, r3, r4, nrow=2, layout_matrix=matrix(c(1,1,1,1,2,2,2,2,2
,3,3,3,3,4,4,4,4,4),
nrow=2, byrow=TRUE)
)## Warning: Removed 2160 rows containing non-finite values (stat_summary).
## Warning: Removed 2160 row(s) containing missing values (geom_path).## Warning: Removed 2160 rows containing non-finite values (stat_summary).## Warning: Removed 2160 row(s) containing missing values (geom_path).#missing values from simulations with low diversity that were filtered out
dev.off()## svg
## 2# removed rows expected based on single-trait simulationsAccuracy of RDA trait prediction as a function of the number of QTN loci in the simulation and strength of selection on the trait. The strength of selection on the trait also determines the strength of selection on the QTN loci in the architecture, how strongly they respond to selection, and the amount of LD around them when they sweep to high frequency in a deme or set of demes.
head(rdapred_df1[,40:50])## Bonf_alpha numCausalLowMAFsample all_corr_phen_temp subsamp_corr_phen_temp
## 1 1.918428e-06 39 0.9545790 0.8893387
## 2 1.918428e-06 39 0.9545790 0.8893387
## 3 1.918428e-06 39 0.9545790 0.8893387
## 4 1.918428e-06 39 0.9545790 0.8893387
## 5 1.918428e-06 39 0.9545790 0.8893387
## 6 1.730523e-06 104 0.9545764 0.9006961
## all_corr_phen_sal subsamp_corr_phen_sal num_causal_prefilter
## 1 NA NA 2628
## 2 NA NA 2628
## 3 NA NA 2628
## 4 NA NA 2628
## 5 NA NA 2628
## 6 NA NA 3144
## num_causal_postfilter num_non_causal num_neut_prefilter num_neut_postfilter
## 1 310 25753 26587 26587
## 2 310 25753 26587 26587
## 3 310 25753 26587 26587
## 4 310 25753 26587 26587
## 5 310 25753 26587 26587
## 6 382 28511 30284 30284final.df$SIGMA_K_2 <- as.factor(final.df$SIGMA_K_2)
levels(final.df$demog_level_sub)## [1] "N equal\nm constant" "N equal\nm breaks"
## [3] "N latitude cline\nm constant" "N central cline\nm constant"
## [5] "N variable\nm variable"# Remove the demography that had the worse performance in Env2
# so as not to bias the loess smoother
final.df1 <- final.df %>% filter(demog_level_sub != "N variable\nm variable")
rr1 <- ggplot(final.df1) + geom_point(aes(x=num_causal_postfilter, y=RDA_cor_RDA20000temppredict_tempPhen, color=SIGMA_K_2), alpha=0.4) +
geom_smooth(aes(x=num_causal_postfilter, y=RDA_cor_RDA20000temppredict_tempPhen, color=SIGMA_K_2), method="loess", span=10) + scale_x_log10() + ylim(0,1) + ggtheme + ylab("Accuracy of RDA prediction\nTemperature trait\nworse <<--->> better") + guides(color=guide_legend(title="Strength of\nstabilizing\nselection")) + xlab("Number of evolved QTN loci") + ggtitle("A) Accuracy for Temp. trait") + scale_color_hue(labels=c(expression(paste("Strong ", sigma[K]," = 0.5")),
expression(paste("Weak ", sigma[K]," = 4.0"))))
rr1## `geom_smooth()` using formula 'y ~ x'
rr2 <- ggplot(final.df1) + geom_point(aes(x=num_causal_postfilter, y=RDA_cor_RDA20000salpredict_salPhen, color=demog_level), alpha=0.4) +
geom_smooth(aes(x=num_causal_postfilter, y=RDA_cor_RDA20000salpredict_salPhen, color=demog_level), method="loess", span=20) + scale_x_log10() + ylim(0,1) + ggtheme + ylab("Accuracy of RDA prediction\nEnv2 trait\nworse <<--->> better") + guides(color=guide_legend(title="Landscape")) + xlab("Number of evolved QTN loci") + ggtitle("B) Accuracy for Env2 trait (always strong selection)") #missing values expected for Env2 trait because not under selection in all sims
rr2## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 360 rows containing non-finite values (stat_smooth).## Warning: Removed 360 rows containing missing values (geom_point).
pdf(paste0(outputs,"RDAtraitpred_vs_nQTN.pdf"), width=10, height=5)
grid.arrange(rr1, rr2, nrow=1)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).dev.off()## svg
## 2